{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/object-detection/papers/ran/2","list_of":"/task/object-detection","task":"Object Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":2,"pages_in_order":12,"rows_per_page":100,"rows":[101,200],"of":1183,"counts":{"archive_papers_tagged":10957,"with_a_code_link":4657,"where_syntology_ran_a_sample":1183,"not_listed_spam_title":0,"listed":10957,"listed_where_code_ran":1183,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1038,"every_run_a_failure_of_syntologys_instrument":145,"listed_with_a_run_with_no_instrument_failure":1038,"listed_every_run_a_failure_of_syntologys_instrument":145,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/object-detection/papers/ran/1","prev":"/task/object-detection/papers/ran/1","next":"/task/object-detection/papers/ran/3","papers":[{"url":"/paper/scaling-graph-convolutions-for-mobile-vision","slug":"scaling-graph-convolutions-for-mobile-vision","title":"Scaling Graph Convolutions for Mobile Vision","date":"2024-06-09","arxiv_id":"2406.05850","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scaling-graph-convolutions-for-mobile-vision#ran","syntology_url":"https://syntology.ai/paper/2406.05850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05850"}},"official":{"repos":["sldgroup/mobilevigv2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lw-detr-a-transformer-replacement-to-yolo-for","slug":"lw-detr-a-transformer-replacement-to-yolo-for","title":"LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection","date":"2024-06-05","arxiv_id":"2406.03459","repositories_listed":2,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":4,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/lw-detr-a-transformer-replacement-to-yolo-for#ran","syntology_url":"https://syntology.ai/paper/2406.03459","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03459"}},"official":{"repos":["atten4vis/lw-detr"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/grootvl-tree-topology-is-all-you-need-in","slug":"grootvl-tree-topology-is-all-you-need-in","title":"GrootVL: Tree Topology is All You Need in State Space Model","date":"2024-06-04","arxiv_id":"2406.02395","repositories_listed":2,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":14,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/grootvl-tree-topology-is-all-you-need-in#ran","syntology_url":"https://syntology.ai/paper/2406.02395","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02395"}},"official":{"repos":["easonxiao-888/grootvl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/open-yolo-3d-towards-fast-and-accurate-open","slug":"open-yolo-3d-towards-fast-and-accurate-open","title":"Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation","date":"2024-06-04","arxiv_id":"2406.02548","repositories_listed":1,"syntology":{"n":11,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":11,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/open-yolo-3d-towards-fast-and-accurate-open#ran","syntology_url":"https://syntology.ai/paper/2406.02548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02548"}},"official":{"repos":["aminebdj/openyolo3d"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/geminifusion-efficient-pixel-wise-multimodal","slug":"geminifusion-efficient-pixel-wise-multimodal","title":"GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer","date":"2024-06-03","arxiv_id":"2406.01210","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/geminifusion-efficient-pixel-wise-multimodal#ran","syntology_url":"https://syntology.ai/paper/2406.01210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01210"}},"official":{"repos":["jiadingcn/geminifusion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/collaborative-novel-object-discovery-and-box","slug":"collaborative-novel-object-discovery-and-box","title":"Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection","date":"2024-06-02","arxiv_id":"2406.00830","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/collaborative-novel-object-discovery-and-box#ran","syntology_url":"https://syntology.ai/paper/2406.00830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00830"}},"official":{"repos":["yangcaoai/CoDA_NeurIPS2023"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/on-calibration-of-object-detectors-pitfalls","slug":"on-calibration-of-object-detectors-pitfalls","title":"On Calibration of Object Detectors: Pitfalls, Evaluation and Baselines","date":"2024-05-30","arxiv_id":"2405.20459","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-calibration-of-object-detectors-pitfalls#ran","syntology_url":"https://syntology.ai/paper/2405.20459","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20459"}},"official":{"repos":["fiveai/detection_calibration"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ov-dquo-open-vocabulary-detr-with-denoising","slug":"ov-dquo-open-vocabulary-detr-with-denoising","title":"OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision","date":"2024-05-28","arxiv_id":"2405.17913","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ov-dquo-open-vocabulary-detr-with-denoising#ran","syntology_url":"https://syntology.ai/paper/2405.17913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17913"}},"official":{"repos":["xiaomoguhz/ov-dquo"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/oed-towards-one-stage-end-to-end-dynamic","slug":"oed-towards-one-stage-end-to-end-dynamic","title":"OED: Towards One-stage End-to-End Dynamic Scene Graph Generation","date":"2024-05-27","arxiv_id":"2405.16925","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/oed-towards-one-stage-end-to-end-dynamic#ran","syntology_url":"https://syntology.ai/paper/2405.16925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16925"}},"official":{"repos":["guanw-pku/oed"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hardness-aware-scene-synthesis-for-semi","slug":"hardness-aware-scene-synthesis-for-semi","title":"Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection","date":"2024-05-27","arxiv_id":"2405.17422","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hardness-aware-scene-synthesis-for-semi#ran","syntology_url":"https://syntology.ai/paper/2405.17422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17422"}},"official":{"repos":["wzzheng/hass"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffubox-refining-3d-object-detection-with","slug":"diffubox-refining-3d-object-detection-with","title":"DiffuBox: Refining 3D Object Detection with Point Diffusion","date":"2024-05-25","arxiv_id":"2405.16034","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":11,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffubox-refining-3d-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2405.16034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16034"}},"official":{"repos":["cxy1997/DiffuBox"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/yolov10-real-time-end-to-end-object-detection","slug":"yolov10-real-time-end-to-end-object-detection","title":"YOLOv10: Real-Time End-to-End Object Detection","date":"2024-05-23","arxiv_id":"2405.14458","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/yolov10-real-time-end-to-end-object-detection#ran","syntology_url":"https://syntology.ai/paper/2405.14458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14458"}},"official":{"repos":["THU-MIG/yolov10"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/improving-single-domain-generalized-object","slug":"improving-single-domain-generalized-object","title":"Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment","date":"2024-05-23","arxiv_id":"2405.14497","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-single-domain-generalized-object#ran","syntology_url":"https://syntology.ai/paper/2405.14497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14497"}},"official":{"repos":["msohaildanish/divalign"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-detect-and-segment-mobile-objects","slug":"learning-to-detect-and-segment-mobile-objects","title":"MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos","date":"2024-05-23","arxiv_id":"2405.14841","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-to-detect-and-segment-mobile-objects#ran","syntology_url":"https://syntology.ai/paper/2405.14841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14841"}},"official":{"repos":["yihongsun/mod-uv"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/slab-efficient-transformers-with-simplified","slug":"slab-efficient-transformers-with-simplified","title":"SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization","date":"2024-05-19","arxiv_id":"2405.11582","repositories_listed":3,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":8,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":7,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/slab-efficient-transformers-with-simplified#ran","syntology_url":"https://syntology.ai/paper/2405.11582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.11582"}},"official":{"repos":["xinghaochen/slab","mindspore-lab/models"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/size-invariance-matters-rethinking-metrics","slug":"size-invariance-matters-rethinking-metrics","title":"Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection","date":"2024-05-16","arxiv_id":"2405.09782","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":15,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/size-invariance-matters-rethinking-metrics#ran","syntology_url":"https://syntology.ai/paper/2405.09782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.09782"}},"official":{"repos":["ferry-li/si-sod"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/grounding-dino-1-5-advance-the-edge-of-open","slug":"grounding-dino-1-5-advance-the-edge-of-open","title":"Grounding DINO 1.5: Advance the \"Edge\" of Open-Set Object Detection","date":"2024-05-16","arxiv_id":"2405.10300","repositories_listed":3,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/grounding-dino-1-5-advance-the-edge-of-open#ran","syntology_url":"https://syntology.ai/paper/2405.10300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.10300"}},"official":{"repos":["idea-research/grounding-dino-1.5-api"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/grounded-3d-llm-with-referent-tokens","slug":"grounded-3d-llm-with-referent-tokens","title":"Grounded 3D-LLM with Referent Tokens","date":"2024-05-16","arxiv_id":"2405.10370","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":17,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/grounded-3d-llm-with-referent-tokens#ran","syntology_url":"https://syntology.ai/paper/2405.10370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.10370"}},"official":{"repos":["OpenRobotLab/Grounded_3D-LLM"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/differentiable-model-scaling-using","slug":"differentiable-model-scaling-using","title":"Differentiable Model Scaling using Differentiable Topk","date":"2024-05-12","arxiv_id":"2405.07194","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/differentiable-model-scaling-using#ran","syntology_url":"https://syntology.ai/paper/2405.07194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07194"}},"official":{"repos":["LKJacky/Differentiable-Model-Scaling"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/building-a-strong-pre-training-baseline-for","slug":"building-a-strong-pre-training-baseline-for","title":"Building a Strong Pre-Training Baseline for Universal 3D Large-Scale Perception","date":"2024-05-12","arxiv_id":"2405.07201","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/building-a-strong-pre-training-baseline-for#ran","syntology_url":"https://syntology.ai/paper/2405.07201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07201"}},"official":{"repos":["chenhaomingbob/csc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ptq4sam-post-training-quantization-for","slug":"ptq4sam-post-training-quantization-for","title":"PTQ4SAM: Post-Training Quantization for Segment Anything","date":"2024-05-06","arxiv_id":"2405.03144","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ptq4sam-post-training-quantization-for#ran","syntology_url":"https://syntology.ai/paper/2405.03144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.03144"}},"official":{"repos":["chengtao-lv/ptq4sam"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unifs-universal-few-shot-instance-perception","slug":"unifs-universal-few-shot-instance-perception","title":"UniFS: Universal Few-shot Instance Perception with Point Representations","date":"2024-04-30","arxiv_id":"2404.19401","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unifs-universal-few-shot-instance-perception#ran","syntology_url":"https://syntology.ai/paper/2404.19401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.19401"}},"official":{"repos":["jin-s13/unifs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cfmw-cross-modality-fusion-mamba-for","slug":"cfmw-cross-modality-fusion-mamba-for","title":"CFMW: Cross-modality Fusion Mamba for Multispectral Object Detection under Adverse Weather Conditions","date":"2024-04-25","arxiv_id":"2404.16302","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cfmw-cross-modality-fusion-mamba-for#ran","syntology_url":"https://syntology.ai/paper/2404.16302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16302"}},"official":{"repos":["lhy-zjut/cfmw"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/commonsense-prototype-for-outdoor","slug":"commonsense-prototype-for-outdoor","title":"Commonsense Prototype for Outdoor Unsupervised 3D Object Detection","date":"2024-04-25","arxiv_id":"2404.16493","repositories_listed":1,"syntology":{"n":20,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":20,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/commonsense-prototype-for-outdoor#ran","syntology_url":"https://syntology.ai/paper/2404.16493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16493"}},"official":{"repos":["hailanyi/cpd"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/progressive-token-length-scaling-in","slug":"progressive-token-length-scaling-in","title":"Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation","date":"2024-04-23","arxiv_id":"2404.14657","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/progressive-token-length-scaling-in#ran","syntology_url":"https://syntology.ai/paper/2404.14657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14657"}},"official":{"repos":["abhishekaich27/proscale-pytorch"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unified-unsupervised-salient-object-detection","slug":"unified-unsupervised-salient-object-detection","title":"Unified Unsupervised Salient Object Detection via Knowledge Transfer","date":"2024-04-23","arxiv_id":"2404.14759","repositories_listed":2,"syntology":{"n":19,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/unified-unsupervised-salient-object-detection#ran","syntology_url":"https://syntology.ai/paper/2404.14759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14759"}},"official":{"repos":["I2-Multimedia-Lab/A2S-v3"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/the-devil-is-in-the-object-boundary-towards","slug":"the-devil-is-in-the-object-boundary-towards","title":"The devil is in the object boundary: towards annotation-free instance segmentation using Foundation Models","date":"2024-04-18","arxiv_id":"2404.11957","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-devil-is-in-the-object-boundary-towards#ran","syntology_url":"https://syntology.ai/paper/2404.11957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11957"}},"official":{"repos":["chengshiest/zip-your-clip"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ghostnetv3-exploring-the-training-strategies","slug":"ghostnetv3-exploring-the-training-strategies","title":"GhostNetV3: Exploring the Training Strategies for Compact Models","date":"2024-04-17","arxiv_id":"2404.11202","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ghostnetv3-exploring-the-training-strategies#ran","syntology_url":"https://syntology.ai/paper/2404.11202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11202"}},"official":null}},{"url":"/paper/mambadfuse-a-mamba-based-dual-phase-model-for","slug":"mambadfuse-a-mamba-based-dual-phase-model-for","title":"MambaDFuse: A Mamba-based Dual-phase Model for Multi-modality Image Fusion","date":"2024-04-12","arxiv_id":"2404.08406","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mambadfuse-a-mamba-based-dual-phase-model-for#ran","syntology_url":"https://syntology.ai/paper/2404.08406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.08406"}},"official":{"repos":["Lizhe1228/MambaDFuse"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/airshot-efficient-few-shot-detection-for","slug":"airshot-efficient-few-shot-detection-for","title":"AirShot: Efficient Few-Shot Detection for Autonomous Exploration","date":"2024-04-07","arxiv_id":"2404.05069","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/airshot-efficient-few-shot-detection-for#ran","syntology_url":"https://syntology.ai/paper/2404.05069","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05069"}},"official":{"repos":["imnotprepared/airshot"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/monocd-monocular-3d-object-detection-with","slug":"monocd-monocular-3d-object-detection-with","title":"MonoCD: Monocular 3D Object Detection with Complementary Depths","date":"2024-04-04","arxiv_id":"2404.03181","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":1,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/monocd-monocular-3d-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2404.03181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03181"}},"official":{"repos":["elvintanhust/monocd"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/is-clip-the-main-roadblock-for-fine-grained","slug":"is-clip-the-main-roadblock-for-fine-grained","title":"Is CLIP the main roadblock for fine-grained open-world perception?","date":"2024-04-04","arxiv_id":"2404.03539","repositories_listed":2,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":12,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":17,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/is-clip-the-main-roadblock-for-fine-grained#ran","syntology_url":"https://syntology.ai/paper/2404.03539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03539"}},"official":{"repos":["lorebianchi98/fg-clip"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/dpft-dual-perspective-fusion-transformer-for","slug":"dpft-dual-perspective-fusion-transformer-for","title":"DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection","date":"2024-04-03","arxiv_id":"2404.03015","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dpft-dual-perspective-fusion-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2404.03015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03015"}},"official":{"repos":["tumftm/dpft"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-image-super-resolution-for-image","slug":"beyond-image-super-resolution-for-image","title":"Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss","date":"2024-04-02","arxiv_id":"2404.01692","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":13,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/beyond-image-super-resolution-for-image#ran","syntology_url":"https://syntology.ai/paper/2404.01692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01692"}},"official":{"repos":["jaehakim97/sr4ir"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/disentangled-pre-training-for-human-object","slug":"disentangled-pre-training-for-human-object","title":"Disentangled Pre-training for Human-Object Interaction Detection","date":"2024-04-02","arxiv_id":"2404.01725","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/disentangled-pre-training-for-human-object#ran","syntology_url":"https://syntology.ai/paper/2404.01725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01725"}},"official":{"repos":["xingaoli/dp-hoi"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/scene-adaptive-sparse-transformer-for-event","slug":"scene-adaptive-sparse-transformer-for-event","title":"Scene Adaptive Sparse Transformer for Event-based Object Detection","date":"2024-04-02","arxiv_id":"2404.01882","repositories_listed":1,"syntology":{"n":23,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":0,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/scene-adaptive-sparse-transformer-for-event#ran","syntology_url":"https://syntology.ai/paper/2404.01882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01882"}},"official":{"repos":["peterande/sast"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/egtr-extracting-graph-from-transformer-for","slug":"egtr-extracting-graph-from-transformer-for","title":"EGTR: Extracting Graph from Transformer for Scene Graph Generation","date":"2024-04-02","arxiv_id":"2404.02072","repositories_listed":1,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":12,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":4,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/egtr-extracting-graph-from-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2404.02072","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02072"}},"official":{"repos":["naver-ai/egtr"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/nerf-mae-masked-autoencoders-for-self","slug":"nerf-mae-masked-autoencoders-for-self","title":"NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields","date":"2024-04-01","arxiv_id":"2404.01300","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nerf-mae-masked-autoencoders-for-self#ran","syntology_url":"https://syntology.ai/paper/2404.01300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01300"}},"official":{"repos":["zubair-irshad/NeRF-MAE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/weak-to-strong-3d-object-detection-with-x-ray","slug":"weak-to-strong-3d-object-detection-with-x-ray","title":"Weak-to-Strong 3D Object Detection with X-Ray Distillation","date":"2024-03-31","arxiv_id":"2404.00679","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/weak-to-strong-3d-object-detection-with-x-ray#ran","syntology_url":"https://syntology.ai/paper/2404.00679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00679"}},"official":{"repos":["sakharok13/x-ray-teacher-patching-tools"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ov-uni3detr-towards-unified-open-vocabulary","slug":"ov-uni3detr-towards-unified-open-vocabulary","title":"OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation","date":"2024-03-28","arxiv_id":"2403.19580","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ov-uni3detr-towards-unified-open-vocabulary#ran","syntology_url":"https://syntology.ai/paper/2403.19580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19580"}},"official":{"repos":["zhenyuw16/uni3detr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/densenets-reloaded-paradigm-shift-beyond","slug":"densenets-reloaded-paradigm-shift-beyond","title":"DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs","date":"2024-03-28","arxiv_id":"2403.19588","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/densenets-reloaded-paradigm-shift-beyond#ran","syntology_url":"https://syntology.ai/paper/2403.19588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19588"}},"official":{"repos":["huggingface/pytorch-image-models","naver-ai/rdnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ship-in-sight-diffusion-models-for-ship-image","slug":"ship-in-sight-diffusion-models-for-ship-image","title":"Ship in Sight: Diffusion Models for Ship-Image Super Resolution","date":"2024-03-27","arxiv_id":"2403.18370","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ship-in-sight-diffusion-models-for-ship-image#ran","syntology_url":"https://syntology.ai/paper/2403.18370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18370"}},"official":{"repos":["luigisigillo/shipinsight"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/benchmarking-object-detectors-with-coco-a-new","slug":"benchmarking-object-detectors-with-coco-a-new","title":"Benchmarking Object Detectors with COCO: A New Path Forward","date":"2024-03-27","arxiv_id":"2403.18819","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/benchmarking-object-detectors-with-coco-a-new#ran","syntology_url":"https://syntology.ai/paper/2403.18819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18819"}},"official":{"repos":["kdexd/coco-rem"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/plainmamba-improving-non-hierarchical-mamba","slug":"plainmamba-improving-non-hierarchical-mamba","title":"PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition","date":"2024-03-26","arxiv_id":"2403.17695","repositories_listed":2,"syntology":{"n":11,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/plainmamba-improving-non-hierarchical-mamba#ran","syntology_url":"https://syntology.ai/paper/2403.17695","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17695"}},"official":{"repos":["chenhongyiyang/plainmamba"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/multiple-object-tracking-as-id-prediction","slug":"multiple-object-tracking-as-id-prediction","title":"Multiple Object Tracking as ID Prediction","date":"2024-03-25","arxiv_id":"2403.16848","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multiple-object-tracking-as-id-prediction#ran","syntology_url":"https://syntology.ai/paper/2403.16848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.16848"}},"official":{"repos":["MCG-NJU/MOTIP"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/optimizing-lidar-placements-for-robust","slug":"optimizing-lidar-placements-for-robust","title":"Is Your LiDAR Placement Optimized for 3D Scene Understanding?","date":"2024-03-25","arxiv_id":"2403.17009","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/optimizing-lidar-placements-for-robust#ran","syntology_url":"https://syntology.ai/paper/2403.17009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17009"}},"official":{"repos":["ywyeli/place3d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/salience-detr-enhancing-detection-transformer-1","slug":"salience-detr-enhancing-detection-transformer-1","title":"Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement","date":"2024-03-24","arxiv_id":"2403.16131","repositories_listed":3,"syntology":{"n":23,"n_ran":11,"n_constructed":6,"n_ran_checked":8,"n_instrument":3,"n_unverified":12,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"11 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/salience-detr-enhancing-detection-transformer-1#ran","syntology_url":"https://syntology.ai/paper/2403.16131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.16131"}},"official":{"repos":["xiuqhou/Salience-DETR"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/is-fusion-instance-scene-collaborative-fusion","slug":"is-fusion-instance-scene-collaborative-fusion","title":"IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection","date":"2024-03-22","arxiv_id":"2403.15241","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/is-fusion-instance-scene-collaborative-fusion#ran","syntology_url":"https://syntology.ai/paper/2403.15241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15241"}},"official":{"repos":["yinjunbo/is-fusion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mtp-advancing-remote-sensing-foundation-model","slug":"mtp-advancing-remote-sensing-foundation-model","title":"MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining","date":"2024-03-20","arxiv_id":"2403.13430","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mtp-advancing-remote-sensing-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2403.13430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.13430"}},"official":{"repos":["vitae-transformer/mtp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/find-n-propagate-open-vocabulary-3d-object","slug":"find-n-propagate-open-vocabulary-3d-object","title":"Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments","date":"2024-03-20","arxiv_id":"2403.13556","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/find-n-propagate-open-vocabulary-3d-object#ran","syntology_url":"https://syntology.ai/paper/2403.13556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.13556"}},"official":{"repos":["djamahl99/findnpropagate"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rar-retrieving-and-ranking-augmented-mllms","slug":"rar-retrieving-and-ranking-augmented-mllms","title":"RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition","date":"2024-03-20","arxiv_id":"2403.13805","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rar-retrieving-and-ranking-augmented-mllms#ran","syntology_url":"https://syntology.ai/paper/2403.13805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.13805"}},"official":{"repos":["liuziyu77/rar"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-region-language-pretraining-for","slug":"generative-region-language-pretraining-for","title":"Generative Region-Language Pretraining for Open-Ended Object Detection","date":"2024-03-15","arxiv_id":"2403.10191","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generative-region-language-pretraining-for#ran","syntology_url":"https://syntology.ai/paper/2403.10191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10191"}},"official":{"repos":["foundationvision/generateu"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/simpb-a-single-model-for-2d-and-3d-object","slug":"simpb-a-single-model-for-2d-and-3d-object","title":"SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras","date":"2024-03-15","arxiv_id":"2403.10353","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/simpb-a-single-model-for-2d-and-3d-object#ran","syntology_url":"https://syntology.ai/paper/2403.10353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10353"}},"official":{"repos":["nullmax-vision/simpb"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/d3t-distinctive-dual-domain-teacher","slug":"d3t-distinctive-dual-domain-teacher","title":"D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection","date":"2024-03-14","arxiv_id":"2403.09359","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/d3t-distinctive-dual-domain-teacher#ran","syntology_url":"https://syntology.ai/paper/2403.09359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09359"}},"official":{"repos":["edwarddo69/d3t"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-bounding-box-uncertainties-via-two","slug":"adaptive-bounding-box-uncertainties-via-two","title":"Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction","date":"2024-03-12","arxiv_id":"2403.07263","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-bounding-box-uncertainties-via-two#ran","syntology_url":"https://syntology.ai/paper/2403.07263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07263"}},"official":{"repos":["alextimans/conformal-od"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/liso-lidar-only-self-supervised-3d-object","slug":"liso-lidar-only-self-supervised-3d-object","title":"LISO: Lidar-only Self-Supervised 3D Object Detection","date":"2024-03-11","arxiv_id":"2403.07071","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/liso-lidar-only-self-supervised-3d-object#ran","syntology_url":"https://syntology.ai/paper/2403.07071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07071"}},"official":{"repos":["baurst/liso"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-3d-object-detection-with-2d","slug":"enhancing-3d-object-detection-with-2d","title":"Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors","date":"2024-03-10","arxiv_id":"2403.06093","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhancing-3d-object-detection-with-2d#ran","syntology_url":"https://syntology.ai/paper/2403.06093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06093"}},"official":{"repos":["nullmax-vision/qaf2d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/v-kd-improving-knowledge-distillation-using","slug":"v-kd-improving-knowledge-distillation-using","title":"$V_kD:$ Improving Knowledge Distillation using Orthogonal Projections","date":"2024-03-10","arxiv_id":"2403.06213","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/v-kd-improving-knowledge-distillation-using#ran","syntology_url":"https://syntology.ai/paper/2403.06213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06213"}},"official":{"repos":["roymiles/vkd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/safdnet-a-simple-and-effective-network-for","slug":"safdnet-a-simple-and-effective-network-for","title":"SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection","date":"2024-03-09","arxiv_id":"2403.05817","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/safdnet-a-simple-and-effective-network-for#ran","syntology_url":"https://syntology.ai/paper/2403.05817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.05817"}},"official":{"repos":["zhanggang001/hednet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/frequency-adaptive-dilated-convolution-for","slug":"frequency-adaptive-dilated-convolution-for","title":"Frequency-Adaptive Dilated Convolution for Semantic Segmentation","date":"2024-03-08","arxiv_id":"2403.05369","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/frequency-adaptive-dilated-convolution-for#ran","syntology_url":"https://syntology.ai/paper/2403.05369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.05369"}},"official":{"repos":["linwei-chen/fadc"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/cn-rma-combined-network-with-ray-marching","slug":"cn-rma-combined-network-with-ray-marching","title":"CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoors Object Detection from Multi-view Images","date":"2024-03-07","arxiv_id":"2403.04198","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cn-rma-combined-network-with-ray-marching#ran","syntology_url":"https://syntology.ai/paper/2403.04198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.04198"}},"official":{"repos":["sercharles/cn-rma"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/flame-diffuser-grounded-wildfire-image","slug":"flame-diffuser-grounded-wildfire-image","title":"FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion","date":"2024-03-06","arxiv_id":"2403.03463","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":7,"n_instrument":6,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":16,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/flame-diffuser-grounded-wildfire-image#ran","syntology_url":"https://syntology.ai/paper/2403.03463","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03463"}},"official":{"repos":["AIS-Clemson/FLAME_SD"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-generalizable-incremental-learning","slug":"zero-shot-generalizable-incremental-learning","title":"Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection","date":"2024-03-04","arxiv_id":"2403.01680","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/zero-shot-generalizable-incremental-learning#ran","syntology_url":"https://syntology.ai/paper/2403.01680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01680"}},"official":{"repos":["jarintotiondin/ziragroundingdino"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mca-moment-channel-attention-networks","slug":"mca-moment-channel-attention-networks","title":"MCA: Moment Channel Attention Networks","date":"2024-03-04","arxiv_id":"2403.01713","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mca-moment-channel-attention-networks#ran","syntology_url":"https://syntology.ai/paper/2403.01713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01713"}},"official":{"repos":["csdllab/mca"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tumtraf-v2x-cooperative-perception-dataset","slug":"tumtraf-v2x-cooperative-perception-dataset","title":"TUMTraf V2X Cooperative Perception Dataset","date":"2024-03-02","arxiv_id":"2403.01316","repositories_listed":3,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tumtraf-v2x-cooperative-perception-dataset#ran","syntology_url":"https://syntology.ai/paper/2403.01316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01316"}},"official":{"repos":["tum-traffic-dataset/coopdet3d","tum-traffic-dataset/tum-traffic-dataset-dev-kit","walzimmer/3d-bat"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dams-detr-dynamic-adaptive-multispectral","slug":"dams-detr-dynamic-adaptive-multispectral","title":"DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion","date":"2024-03-01","arxiv_id":"2403.00326","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dams-detr-dynamic-adaptive-multispectral#ran","syntology_url":"https://syntology.ai/paper/2403.00326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00326"}},"official":{"repos":["gjj45/dams-detr","gjj45/damsdet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/visionllama-a-unified-llama-interface-for","slug":"visionllama-a-unified-llama-interface-for","title":"VisionLLaMA: A Unified LLaMA Backbone for Vision Tasks","date":"2024-03-01","arxiv_id":"2403.00522","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/visionllama-a-unified-llama-interface-for#ran","syntology_url":"https://syntology.ai/paper/2403.00522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00522"}},"official":{"repos":["meituan-automl/visionllama"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/privacy-preserving-autoencoder-for","slug":"privacy-preserving-autoencoder-for","title":"Privacy-Preserving Autoencoder for Collaborative Object Detection","date":"2024-02-29","arxiv_id":"2402.18864","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/privacy-preserving-autoencoder-for#ran","syntology_url":"https://syntology.ai/paper/2402.18864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18864"}},"official":{"repos":["bardia-az/ppa-code"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deyo-detr-with-yolo-for-end-to-end-object","slug":"deyo-detr-with-yolo-for-end-to-end-object","title":"DEYO: DETR with YOLO for End-to-End Object Detection","date":"2024-02-26","arxiv_id":"2402.16370","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deyo-detr-with-yolo-for-end-to-end-object#ran","syntology_url":"https://syntology.ai/paper/2402.16370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16370"}},"official":{"repos":["ouyanghaodong/deyo"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-supervised-open-world-object-detection","slug":"semi-supervised-open-world-object-detection","title":"Semi-supervised Open-World Object Detection","date":"2024-02-25","arxiv_id":"2402.16013","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semi-supervised-open-world-object-detection#ran","syntology_url":"https://syntology.ai/paper/2402.16013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16013"}},"official":{"repos":["sahalshajim/ss-owformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/state-space-models-for-event-cameras","slug":"state-space-models-for-event-cameras","title":"State Space Models for Event Cameras","date":"2024-02-23","arxiv_id":"2402.15584","repositories_listed":2,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":16,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":8,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/state-space-models-for-event-cameras#ran","syntology_url":"https://syntology.ai/paper/2402.15584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15584"}},"official":{"repos":["uzh-rpg/ssms_event_cameras"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/transgop-transformer-based-gaze-object","slug":"transgop-transformer-based-gaze-object","title":"TransGOP: Transformer-Based Gaze Object Prediction","date":"2024-02-21","arxiv_id":"2402.13578","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/transgop-transformer-based-gaze-object#ran","syntology_url":"https://syntology.ai/paper/2402.13578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13578"}},"official":{"repos":["chenxi-guo/transgop"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/yolov9-learning-what-you-want-to-learn-using","slug":"yolov9-learning-what-you-want-to-learn-using","title":"YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information","date":"2024-02-21","arxiv_id":"2402.13616","repositories_listed":5,"syntology":{"n":32,"n_ran":25,"n_constructed":9,"n_ran_checked":24,"n_instrument":1,"n_unverified":7,"n_honours":2,"n_violates":1,"n_no_contract":21,"n_pointer_only":11,"phrase":"25 ran (of which 9 constructed an object rather than computing a result; 24 with no instrument failure: 2 honoured, 1 violated, 21 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/yolov9-learning-what-you-want-to-learn-using#ran","syntology_url":"https://syntology.ai/paper/2402.13616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13616"}},"official":{"repos":["WongKinYiu/YOLO"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["listed","named_in_paper","official"]}}},{"url":"/paper/reinforcement-learning-as-a-parsimonious","slug":"reinforcement-learning-as-a-parsimonious","title":"Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation","date":"2024-02-19","arxiv_id":"2402.11760","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/reinforcement-learning-as-a-parsimonious#ran","syntology_url":"https://syntology.ai/paper/2402.11760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11760"}},"official":{"repos":["scailab/paser"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multicorrupt-a-multi-modal-robustness-dataset","slug":"multicorrupt-a-multi-modal-robustness-dataset","title":"MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection","date":"2024-02-18","arxiv_id":"2402.11677","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multicorrupt-a-multi-modal-robustness-dataset#ran","syntology_url":"https://syntology.ai/paper/2402.11677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11677"}},"official":{"repos":["ika-rwth-aachen/multicorrupt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/revit-enhancing-vision-transformers-with","slug":"revit-enhancing-vision-transformers-with","title":"ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connections","date":"2024-02-17","arxiv_id":"2402.11301","repositories_listed":1,"syntology":{"n":24,"n_ran":22,"n_constructed":0,"n_ran_checked":20,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":20,"n_pointer_only":1,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 0 honoured, 0 violated, 20 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/revit-enhancing-vision-transformers-with#ran","syntology_url":"https://syntology.ai/paper/2402.11301","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11301"}},"official":{"repos":["adiko1997/revit"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":0,"n_ran_no_instrument_failure":20,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/beam-beta-distribution-ray-denoising-for","slug":"beam-beta-distribution-ray-denoising-for","title":"Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection","date":"2024-02-06","arxiv_id":"2402.03634","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/beam-beta-distribution-ray-denoising-for#ran","syntology_url":"https://syntology.ai/paper/2402.03634","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03634"}},"official":{"repos":["liewfeng/beam","liewfeng/raydn"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-domain-few-shot-object-detection-via","slug":"cross-domain-few-shot-object-detection-via","title":"Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector","date":"2024-02-05","arxiv_id":"2402.03094","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cross-domain-few-shot-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2402.03094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03094"}},"official":{"repos":["lovelyqian/CDFSOD-benchmark"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hassod-hierarchical-adaptive-self-supervised-1","slug":"hassod-hierarchical-adaptive-self-supervised-1","title":"HASSOD: Hierarchical Adaptive Self-Supervised Object Detection","date":"2024-02-05","arxiv_id":"2402.03311","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hassod-hierarchical-adaptive-self-supervised-1#ran","syntology_url":"https://syntology.ai/paper/2402.03311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03311"}},"official":{"repos":["shengcao-cao/hassod"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/simada-a-simple-unified-framework-for","slug":"simada-a-simple-unified-framework-for","title":"SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes","date":"2024-01-31","arxiv_id":"2401.17803","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/simada-a-simple-unified-framework-for#ran","syntology_url":"https://syntology.ai/paper/2401.17803","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17803"}},"official":{"repos":["zongzi13545329/simada"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/yolo-world-real-time-open-vocabulary-object","slug":"yolo-world-real-time-open-vocabulary-object","title":"YOLO-World: Real-Time Open-Vocabulary Object Detection","date":"2024-01-30","arxiv_id":"2401.17270","repositories_listed":3,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/yolo-world-real-time-open-vocabulary-object#ran","syntology_url":"https://syntology.ai/paper/2401.17270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17270"}},"official":{"repos":["ailab-cvc/yolo-world"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/mixsup-mixed-grained-supervision-for-label","slug":"mixsup-mixed-grained-supervision-for-label","title":"MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection","date":"2024-01-29","arxiv_id":"2401.16305","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mixsup-mixed-grained-supervision-for-label#ran","syntology_url":"https://syntology.ai/paper/2401.16305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.16305"}},"official":{"repos":["bravegroup/pointsam-for-mixsup"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/shvit-single-head-vision-transformer-with","slug":"shvit-single-head-vision-transformer-with","title":"SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design","date":"2024-01-29","arxiv_id":"2401.16456","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/shvit-single-head-vision-transformer-with#ran","syntology_url":"https://syntology.ai/paper/2401.16456","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.16456"}},"official":{"repos":["ysj9909/SHViT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-of-lidar-3d-point","slug":"self-supervised-learning-of-lidar-3d-point","title":"Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration","date":"2024-01-23","arxiv_id":"2401.12452","repositories_listed":2,"syntology":{"n":26,"n_ran":24,"n_constructed":0,"n_ran_checked":18,"n_instrument":6,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":17,"n_pointer_only":26,"phrase":"24 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 1 violated, 17 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-supervised-learning-of-lidar-3d-point#ran","syntology_url":"https://syntology.ai/paper/2401.12452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.12452"}},"official":{"repos":["eaphan/nclr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/muses-the-multi-sensor-semantic-perception","slug":"muses-the-multi-sensor-semantic-perception","title":"MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty","date":"2024-01-23","arxiv_id":"2401.12761","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":13,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/muses-the-multi-sensor-semantic-perception#ran","syntology_url":"https://syntology.ai/paper/2401.12761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.12761"}},"official":{"repos":["timbroed/MUSES"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-centered-kernel-alignment-in","slug":"rethinking-centered-kernel-alignment-in","title":"Rethinking Centered Kernel Alignment in Knowledge Distillation","date":"2024-01-22","arxiv_id":"2401.11824","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rethinking-centered-kernel-alignment-in#ran","syntology_url":"https://syntology.ai/paper/2401.11824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11824"}},"official":{"repos":["klayand/pcka"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ok-robot-what-really-matters-in-integrating","slug":"ok-robot-what-really-matters-in-integrating","title":"OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics","date":"2024-01-22","arxiv_id":"2401.12202","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ok-robot-what-really-matters-in-integrating#ran","syntology_url":"https://syntology.ai/paper/2401.12202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.12202"}},"official":{"repos":["ok-robot/ok-robot"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/one-step-learning-one-step-review","slug":"one-step-learning-one-step-review","title":"One Step Learning, One Step Review","date":"2024-01-19","arxiv_id":"2401.10962","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/one-step-learning-one-step-review#ran","syntology_url":"https://syntology.ai/paper/2401.10962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10962"}},"official":{"repos":["rainbow-xiao/olor-aaai-2024"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mamba-multi-level-aggregation-via-memory-bank","slug":"mamba-multi-level-aggregation-via-memory-bank","title":"MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection","date":"2024-01-18","arxiv_id":"2401.09923","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mamba-multi-level-aggregation-via-memory-bank#ran","syntology_url":"https://syntology.ai/paper/2401.09923","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.09923"}},"official":{"repos":["guanxiongsun/vfe.pytorch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-latent-diffusion-approach-for","slug":"a-simple-latent-diffusion-approach-for","title":"A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting","date":"2024-01-18","arxiv_id":"2401.10227","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-simple-latent-diffusion-approach-for#ran","syntology_url":"https://syntology.ai/paper/2401.10227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10227"}},"official":{"repos":["segments-ai/latent-diffusion-segmentation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/vision-mamba-efficient-visual-representation","slug":"vision-mamba-efficient-visual-representation","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","date":"2024-01-17","arxiv_id":"2401.09417","repositories_listed":15,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/vision-mamba-efficient-visual-representation#ran","syntology_url":"https://syntology.ai/paper/2401.09417","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.09417"}},"official":{"repos":["hustvl/vim"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/dcdet-dynamic-cross-based-3d-object-detector","slug":"dcdet-dynamic-cross-based-3d-object-detector","title":"DCDet: Dynamic Cross-based 3D Object Detector","date":"2024-01-14","arxiv_id":"2401.07240","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dcdet-dynamic-cross-based-3d-object-detector#ran","syntology_url":"https://syntology.ai/paper/2401.07240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.07240"}},"official":{"repos":["say2l/dcdet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dual-perspective-knowledge-enrichment-for","slug":"dual-perspective-knowledge-enrichment-for","title":"Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object Detection","date":"2024-01-10","arxiv_id":"2401.05011","repositories_listed":1,"syntology":{"n":18,"n_ran":9,"n_constructed":0,"n_ran_checked":2,"n_instrument":7,"n_unverified":9,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":18,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 7 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/dual-perspective-knowledge-enrichment-for#ran","syntology_url":"https://syntology.ai/paper/2401.05011","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.05011"}},"official":{"repos":["tingxueronghua/dpke"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/robofusion-towards-robust-multi-modal-3d","slug":"robofusion-towards-robust-multi-modal-3d","title":"RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM","date":"2024-01-08","arxiv_id":"2401.03907","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robofusion-towards-robust-multi-modal-3d#ran","syntology_url":"https://syntology.ai/paper/2401.03907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03907"}},"official":{"repos":["adept-thu/RoboFusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ms-detr-efficient-detr-training-with-mixed","slug":"ms-detr-efficient-detr-training-with-mixed","title":"MS-DETR: Efficient DETR Training with Mixed Supervision","date":"2024-01-08","arxiv_id":"2401.03989","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ms-detr-efficient-detr-training-with-mixed#ran","syntology_url":"https://syntology.ai/paper/2401.03989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03989"}},"official":{"repos":["atten4vis/ms-detr"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-few-shot-object-detection-with","slug":"revisiting-few-shot-object-detection-with","title":"Revisiting Few-Shot Object Detection with Vision-Language Models","date":"2023-12-22","arxiv_id":"2312.14494","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/revisiting-few-shot-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2312.14494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14494"}},"official":{"repos":["anishmadan23/foundational_fsod"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/deco-query-based-end-to-end-object-detection","slug":"deco-query-based-end-to-end-object-detection","title":"DECO: Query-Based End-to-End Object Detection with ConvNets","date":"2023-12-21","arxiv_id":"2312.13735","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/deco-query-based-end-to-end-object-detection#ran","syntology_url":"https://syntology.ai/paper/2312.13735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.13735"}},"official":{"repos":["xinghaochen/DECO","mindspore-lab/models"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/long-tailed-3d-detection-via-2d-late-fusion","slug":"long-tailed-3d-detection-via-2d-late-fusion","title":"Long-Tailed 3D Detection via Multi-Modal Fusion","date":"2023-12-18","arxiv_id":"2312.10986","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":15,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/long-tailed-3d-detection-via-2d-late-fusion#ran","syntology_url":"https://syntology.ai/paper/2312.10986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.10986"}},"official":{"repos":["mayechi/lt3d-lf"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/clim-contrastive-language-image-mosaic-for","slug":"clim-contrastive-language-image-mosaic-for","title":"CLIM: Contrastive Language-Image Mosaic for Region Representation","date":"2023-12-18","arxiv_id":"2312.11376","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/clim-contrastive-language-image-mosaic-for#ran","syntology_url":"https://syntology.ai/paper/2312.11376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.11376"}},"official":{"repos":["wusize/clim"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fomo-bench-a-multi-modal-multi-scale-and","slug":"fomo-bench-a-multi-modal-multi-scale-and","title":"FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models","date":"2023-12-15","arxiv_id":"2312.10114","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fomo-bench-a-multi-modal-multi-scale-and#ran","syntology_url":"https://syntology.ai/paper/2312.10114","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.10114"}},"official":{"repos":["rolnicklab/fomo-bench"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"65c74c42da375b0e3d3ac1c369ec333ef3e0c3911d26e442542ed6c689f99cbc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}