{"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-1/papers/ran/3","list_of":"/task/object-detection-1","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":3,"pages_in_order":11,"rows_per_page":100,"rows":[201,300],"of":1027,"counts":{"archive_papers_tagged":10514,"with_a_code_link":4285,"where_syntology_ran_a_sample":1027,"not_listed_spam_title":0,"listed":10514,"listed_where_code_ran":1027,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":898,"every_run_a_failure_of_syntologys_instrument":129,"listed_with_a_run_with_no_instrument_failure":898,"listed_every_run_a_failure_of_syntologys_instrument":129,"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-1/papers/ran/1","prev":"/task/object-detection-1/papers/ran/2","next":"/task/object-detection-1/papers/ran/4","papers":[{"url":"/paper/bootstrapping-autonomous-radars-with-self","slug":"bootstrapping-autonomous-radars-with-self","title":"Bootstrapping Autonomous Driving Radars with Self-Supervised Learning","date":"2023-12-07","arxiv_id":"2312.04519","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/bootstrapping-autonomous-radars-with-self#ran","syntology_url":"https://syntology.ai/paper/2312.04519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04519"}},"official":{"repos":["yiduohao/radical"],"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/diffusion-ss3d-diffusion-model-for-semi-1","slug":"diffusion-ss3d-diffusion-model-for-semi-1","title":"Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection","date":"2023-12-05","arxiv_id":"2312.02966","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"5 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/diffusion-ss3d-diffusion-model-for-semi-1#ran","syntology_url":"https://syntology.ai/paper/2312.02966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02966"}},"official":{"repos":["luluho1208/diffusion-ss3d"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/bevnext-reviving-dense-bev-frameworks-for-3d","slug":"bevnext-reviving-dense-bev-frameworks-for-3d","title":"BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection","date":"2023-12-04","arxiv_id":"2312.01696","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/bevnext-reviving-dense-bev-frameworks-for-3d#ran","syntology_url":"https://syntology.ai/paper/2312.01696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01696"}},"official":{"repos":["woxihuanjiangguo/bevnext"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/aligning-and-prompting-everything-all-at-once","slug":"aligning-and-prompting-everything-all-at-once","title":"Aligning and Prompting Everything All at Once for Universal Visual Perception","date":"2023-12-04","arxiv_id":"2312.02153","repositories_listed":2,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"11 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/aligning-and-prompting-everything-all-at-once#ran","syntology_url":"https://syntology.ai/paper/2312.02153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02153"}},"official":{"repos":["shenyunhang/ape"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/g2d-from-global-to-dense-radiography","slug":"g2d-from-global-to-dense-radiography","title":"G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training","date":"2023-12-03","arxiv_id":"2312.01522","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"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) · 5 unverified","sample_list":"/paper/g2d-from-global-to-dense-radiography#ran","syntology_url":"https://syntology.ai/paper/2312.01522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01522"}},"official":{"repos":["cheliu-computation/g2d-neurips24"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/trackdiffusion-multi-object-tracking-data","slug":"trackdiffusion-multi-object-tracking-data","title":"TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models","date":"2023-12-01","arxiv_id":"2312.00651","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":2,"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/trackdiffusion-multi-object-tracking-data#ran","syntology_url":"https://syntology.ai/paper/2312.00651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.00651"}},"official":null}},{"url":"/paper/the-devil-is-in-the-fine-grained-details","slug":"the-devil-is-in-the-fine-grained-details","title":"The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding","date":"2023-11-29","arxiv_id":"2311.17518","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-devil-is-in-the-fine-grained-details#ran","syntology_url":"https://syntology.ai/paper/2311.17518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17518"}},"official":{"repos":["lorebianchi98/fg-ovd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/do-text-free-diffusion-models-learn","slug":"do-text-free-diffusion-models-learn","title":"Do text-free diffusion models learn discriminative visual representations?","date":"2023-11-29","arxiv_id":"2311.17921","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 3 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/do-text-free-diffusion-models-learn#ran","syntology_url":"https://syntology.ai/paper/2311.17921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17921"}},"official":{"repos":["soumik-kanad/diffssl"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/transnext-robust-foveal-visual-perception-for","slug":"transnext-robust-foveal-visual-perception-for","title":"TransNeXt: Robust Foveal Visual Perception for Vision Transformers","date":"2023-11-28","arxiv_id":"2311.17132","repositories_listed":4,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"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, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transnext-robust-foveal-visual-perception-for#ran","syntology_url":"https://syntology.ai/paper/2311.17132","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17132"}},"official":{"repos":["daishiresearch/transnext"],"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":["listed","official"]}}},{"url":"/paper/bursting-spikes-efficient-and-high","slug":"bursting-spikes-efficient-and-high","title":"Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks","date":"2023-11-24","arxiv_id":"2311.14265","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"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) · 0 unverified","sample_list":"/paper/bursting-spikes-efficient-and-high#ran","syntology_url":"https://syntology.ai/paper/2311.14265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14265"}},"official":{"repos":["bic-l/burst-ann2snn"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/instruct-me-more-random-prompting-for-visual","slug":"instruct-me-more-random-prompting-for-visual","title":"Instruct Me More! Random Prompting for Visual In-Context Learning","date":"2023-11-07","arxiv_id":"2311.03648","repositories_listed":1,"syntology":{"n":9,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":9,"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) · 7 unverified","sample_list":"/paper/instruct-me-more-random-prompting-for-visual#ran","syntology_url":"https://syntology.ai/paper/2311.03648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03648"}},"official":{"repos":["jackieam/inmemo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/flow-based-feature-fusion-for-vehicle-1","slug":"flow-based-feature-fusion-for-vehicle-1","title":"Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection","date":"2023-11-03","arxiv_id":"2311.01682","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/flow-based-feature-fusion-for-vehicle-1#ran","syntology_url":"https://syntology.ai/paper/2311.01682","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01682"}},"official":{"repos":["haibao-yu/ffnet-vic3d"],"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/effective-human-ai-teams-via-learned-natural-1","slug":"effective-human-ai-teams-via-learned-natural-1","title":"Effective Human-AI Teams via Learned Natural Language Rules and Onboarding","date":"2023-11-02","arxiv_id":"2311.01007","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":12,"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) · 4 unverified","sample_list":"/paper/effective-human-ai-teams-via-learned-natural-1#ran","syntology_url":"https://syntology.ai/paper/2311.01007","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01007"}},"official":{"repos":["clinicalml/onboarding_human_ai"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/recognize-any-regions","slug":"recognize-any-regions","title":"Recognize Any Regions","date":"2023-11-02","arxiv_id":"2311.01373","repositories_listed":1,"syntology":{"n":11,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":11,"phrase":"5 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; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/recognize-any-regions#ran","syntology_url":"https://syntology.ai/paper/2311.01373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01373"}},"official":{"repos":["surrey-uplab/recognize-any-regions"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/tpsence-towards-artifact-free-realistic-rain","slug":"tpsence-towards-artifact-free-realistic-rain","title":"TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain","date":"2023-11-01","arxiv_id":"2311.00660","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tpsence-towards-artifact-free-realistic-rain#ran","syntology_url":"https://syntology.ai/paper/2311.00660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.00660"}},"official":{"repos":["shenzheng2000/tpsence"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-high-resolution-dataset-for-instance","slug":"a-high-resolution-dataset-for-instance","title":"A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture","date":"2023-10-30","arxiv_id":"2310.19257","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/a-high-resolution-dataset-for-instance#ran","syntology_url":"https://syntology.ai/paper/2310.19257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.19257"}},"official":{"repos":["insdet/instance-detection"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/battle-of-the-backbones-a-large-scale","slug":"battle-of-the-backbones-a-large-scale","title":"Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks","date":"2023-10-30","arxiv_id":"2310.19909","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/battle-of-the-backbones-a-large-scale#ran","syntology_url":"https://syntology.ai/paper/2310.19909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.19909"}},"official":{"repos":["hsouri/battle-of-the-backbones"],"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/probed-proactive-object-detection-wrapper","slug":"probed-proactive-object-detection-wrapper","title":"PrObeD: Proactive Object Detection Wrapper","date":"2023-10-28","arxiv_id":"2310.18788","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"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 1 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/probed-proactive-object-detection-wrapper#ran","syntology_url":"https://syntology.ai/paper/2310.18788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18788"}},"official":null}},{"url":"/paper/navigating-data-heterogeneity-in-federated","slug":"navigating-data-heterogeneity-in-federated","title":"Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection","date":"2023-10-26","arxiv_id":"2310.17097","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/navigating-data-heterogeneity-in-federated#ran","syntology_url":"https://syntology.ai/paper/2310.17097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17097"}},"official":{"repos":["Kthyeon/ssfod"],"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/lp-ovod-open-vocabulary-object-detection-by","slug":"lp-ovod-open-vocabulary-object-detection-by","title":"LP-OVOD: Open-Vocabulary Object Detection by Linear Probing","date":"2023-10-26","arxiv_id":"2310.17109","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":2,"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/lp-ovod-open-vocabulary-object-detection-by#ran","syntology_url":"https://syntology.ai/paper/2310.17109","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17109"}},"official":{"repos":["vinairesearch/lp-ovod"],"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/codet-co-occurrence-guided-region-word-1","slug":"codet-co-occurrence-guided-region-word-1","title":"CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection","date":"2023-10-25","arxiv_id":"2310.16667","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":8,"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) · 2 unverified","sample_list":"/paper/codet-co-occurrence-guided-region-word-1#ran","syntology_url":"https://syntology.ai/paper/2310.16667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16667"}},"official":{"repos":["cvmi-lab/codet"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-vision-centric-multi-modal-1","slug":"leveraging-vision-centric-multi-modal-1","title":"Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection","date":"2023-10-24","arxiv_id":"2310.15670","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/leveraging-vision-centric-multi-modal-1#ran","syntology_url":"https://syntology.ai/paper/2310.15670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15670"}},"official":{"repos":["opendrivelab/birds-eye-view-perception"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/safe-navigation-training-autonomous-vehicles","slug":"safe-navigation-training-autonomous-vehicles","title":"Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA","date":"2023-10-23","arxiv_id":"2311.10735","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/safe-navigation-training-autonomous-vehicles#ran","syntology_url":"https://syntology.ai/paper/2311.10735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.10735"}},"official":{"repos":["tejas-deo/safe-navigation-training-autonomous-vehicles-using-deep-reinforcement-learning-in-carla"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/scalablemap-scalable-map-learning-for-online","slug":"scalablemap-scalable-map-learning-for-online","title":"ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction","date":"2023-10-20","arxiv_id":"2310.13378","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"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) · 1 unverified","sample_list":"/paper/scalablemap-scalable-map-learning-for-online#ran","syntology_url":"https://syntology.ai/paper/2310.13378","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13378"}},"official":{"repos":["jingy1yu/scalablemap"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-from-rich-semantics-and-coarse","slug":"learning-from-rich-semantics-and-coarse","title":"Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection","date":"2023-10-18","arxiv_id":"2310.12152","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-from-rich-semantics-and-coarse#ran","syntology_url":"https://syntology.ai/paper/2310.12152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.12152"}},"official":{"repos":["MengLcool/RichSem"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/geneval-an-object-focused-framework-for","slug":"geneval-an-object-focused-framework-for","title":"GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment","date":"2023-10-17","arxiv_id":"2310.11513","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":0,"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/geneval-an-object-focused-framework-for#ran","syntology_url":"https://syntology.ai/paper/2310.11513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.11513"}},"official":{"repos":["djghosh13/geneval"],"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/rank-detr-for-high-quality-object-detection","slug":"rank-detr-for-high-quality-object-detection","title":"Rank-DETR for High Quality Object Detection","date":"2023-10-13","arxiv_id":"2310.08854","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/rank-detr-for-high-quality-object-detection#ran","syntology_url":"https://syntology.ai/paper/2310.08854","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.08854"}},"official":{"repos":["leaplabthu/rank-detr"],"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/dual-radar-a-multi-modal-dataset-with-dual-4d","slug":"dual-radar-a-multi-modal-dataset-with-dual-4d","title":"Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving","date":"2023-10-11","arxiv_id":"2310.07602","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/dual-radar-a-multi-modal-dataset-with-dual-4d#ran","syntology_url":"https://syntology.ai/paper/2310.07602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07602"}},"official":{"repos":["adept-thu/dual-radar"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/instructdet-diversifying-referring-object","slug":"instructdet-diversifying-referring-object","title":"InstructDET: Diversifying Referring Object Detection with Generalized Instructions","date":"2023-10-08","arxiv_id":"2310.05136","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/instructdet-diversifying-referring-object#ran","syntology_url":"https://syntology.ai/paper/2310.05136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05136"}},"official":{"repos":["jyfenggogo/instructdet"],"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/magicdrive-street-view-generation-with","slug":"magicdrive-street-view-generation-with","title":"MagicDrive: Street View Generation with Diverse 3D Geometry Control","date":"2023-10-04","arxiv_id":"2310.02601","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/magicdrive-street-view-generation-with#ran","syntology_url":"https://syntology.ai/paper/2310.02601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02601"}},"official":null}},{"url":"/paper/get-group-event-transformer-for-event-based-1","slug":"get-group-event-transformer-for-event-based-1","title":"GET: Group Event Transformer for Event-Based Vision","date":"2023-10-04","arxiv_id":"2310.02642","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":6,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"8 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/get-group-event-transformer-for-event-based-1#ran","syntology_url":"https://syntology.ai/paper/2310.02642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02642"}},"official":{"repos":["peterande/get-group-event-transformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/darth-holistic-test-time-adaptation-for-1","slug":"darth-holistic-test-time-adaptation-for-1","title":"DARTH: Holistic Test-time Adaptation for Multiple Object Tracking","date":"2023-10-03","arxiv_id":"2310.01926","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/darth-holistic-test-time-adaptation-for-1#ran","syntology_url":"https://syntology.ai/paper/2310.01926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01926"}},"official":{"repos":["mattiasegu/darth"],"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/clipself-vision-transformer-distills-itself","slug":"clipself-vision-transformer-distills-itself","title":"CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction","date":"2023-10-02","arxiv_id":"2310.01403","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"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) · 2 unverified","sample_list":"/paper/clipself-vision-transformer-distills-itself#ran","syntology_url":"https://syntology.ai/paper/2310.01403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01403"}},"official":{"repos":["wusize/clipself"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-visual-scene-understanding","slug":"adaptive-visual-scene-understanding","title":"Adaptive Visual Scene Understanding: Incremental Scene Graph Generation","date":"2023-10-02","arxiv_id":"2310.01636","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/adaptive-visual-scene-understanding#ran","syntology_url":"https://syntology.ai/paper/2310.01636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01636"}},"official":{"repos":["zhanglab-deepneurocoglab/csegg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-of-contextualized","slug":"self-supervised-learning-of-contextualized","title":"Self-supervised Learning of Contextualized Local Visual Embeddings","date":"2023-10-01","arxiv_id":"2310.00527","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"8 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-learning-of-contextualized#ran","syntology_url":"https://syntology.ai/paper/2310.00527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00527"}},"official":{"repos":["sthalles/clove"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/horizontal-class-backdoor-to-deep-learning","slug":"horizontal-class-backdoor-to-deep-learning","title":"Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense","date":"2023-10-01","arxiv_id":"2310.00542","repositories_listed":1,"syntology":{"n":22,"n_ran":16,"n_constructed":0,"n_ran_checked":10,"n_instrument":6,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":7,"phrase":"16 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; 6 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/horizontal-class-backdoor-to-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2310.00542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00542"}},"official":{"repos":["shihe98/hcb"],"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":["found_in_text","official"]}}},{"url":"/paper/instructcv-instruction-tuned-text-to-image","slug":"instructcv-instruction-tuned-text-to-image","title":"InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists","date":"2023-09-30","arxiv_id":"2310.00390","repositories_listed":1,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":12,"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) · 7 unverified","sample_list":"/paper/instructcv-instruction-tuned-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2310.00390","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00390"}},"official":{"repos":["AlaaLab/InstructCV"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/see-beyond-seeing-robust-3d-object-detection","slug":"see-beyond-seeing-robust-3d-object-detection","title":"Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation","date":"2023-09-29","arxiv_id":"2309.17336","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"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) · 2 unverified","sample_list":"/paper/see-beyond-seeing-robust-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/2309.17336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.17336"}},"official":{"repos":["djning/see_beyond_seeing"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/score-submodular-combinatorial-representation","slug":"score-submodular-combinatorial-representation","title":"SCoRe: Submodular Combinatorial Representation Learning","date":"2023-09-29","arxiv_id":"2310.00165","repositories_listed":0,"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/score-submodular-combinatorial-representation#ran","syntology_url":"https://syntology.ai/paper/2310.00165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00165"}},"official":null}},{"url":"/paper/mocae-mixture-of-calibrated-experts","slug":"mocae-mixture-of-calibrated-experts","title":"MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection","date":"2023-09-26","arxiv_id":"2309.14976","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/mocae-mixture-of-calibrated-experts#ran","syntology_url":"https://syntology.ai/paper/2309.14976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14976"}},"official":{"repos":["fiveai/MoCaE"],"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/distillbev-boosting-multi-camera-3d-object","slug":"distillbev-boosting-multi-camera-3d-object","title":"DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation","date":"2023-09-26","arxiv_id":"2309.15109","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/distillbev-boosting-multi-camera-3d-object#ran","syntology_url":"https://syntology.ai/paper/2309.15109","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.15109"}},"official":{"repos":["qcraftai/distill-bev"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unibev-multi-modal-3d-object-detection-with","slug":"unibev-multi-modal-3d-object-detection-with","title":"UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor Modalities","date":"2023-09-25","arxiv_id":"2309.14516","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":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/unibev-multi-modal-3d-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2309.14516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14516"}},"official":{"repos":["tudelft-iv/unibev"],"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/detect-every-thing-with-few-examples","slug":"detect-every-thing-with-few-examples","title":"Detect Everything with Few Examples","date":"2023-09-22","arxiv_id":"2309.12969","repositories_listed":1,"syntology":{"n":20,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":1,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/detect-every-thing-with-few-examples#ran","syntology_url":"https://syntology.ai/paper/2309.12969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.12969"}},"official":{"repos":["mlzxy/devit"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/shape-anchor-guided-holistic-indoor-scene","slug":"shape-anchor-guided-holistic-indoor-scene","title":"Shape Anchor Guided Holistic Indoor Scene Understanding","date":"2023-09-20","arxiv_id":"2309.11133","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":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) · 1 unverified","sample_list":"/paper/shape-anchor-guided-holistic-indoor-scene#ran","syntology_url":"https://syntology.ai/paper/2309.11133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11133"}},"official":{"repos":["Geo-Tell/AncRec"],"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/rmt-retentive-networks-meet-vision","slug":"rmt-retentive-networks-meet-vision","title":"RMT: Retentive Networks Meet Vision Transformers","date":"2023-09-20","arxiv_id":"2309.11523","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":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/rmt-retentive-networks-meet-vision#ran","syntology_url":"https://syntology.ai/paper/2309.11523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11523"}},"official":{"repos":["qhfan/RMT"],"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/beyond-generation-harnessing-text-to-image","slug":"beyond-generation-harnessing-text-to-image","title":"Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation","date":"2023-09-12","arxiv_id":"2309.05956","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":9,"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/beyond-generation-harnessing-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2309.05956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05956"}},"official":{"repos":["gyhandy/text2image-for-detection"],"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/objectlab-automated-diagnosis-of-mislabeled","slug":"objectlab-automated-diagnosis-of-mislabeled","title":"ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data","date":"2023-09-02","arxiv_id":"2309.00832","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/objectlab-automated-diagnosis-of-mislabeled#ran","syntology_url":"https://syntology.ai/paper/2309.00832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00832"}},"official":{"repos":["cleanlab/cleanlab"],"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/openins3d-snap-and-lookup-for-3d-open","slug":"openins3d-snap-and-lookup-for-3d-open","title":"OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation","date":"2023-09-01","arxiv_id":"2309.00616","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/openins3d-snap-and-lookup-for-3d-open#ran","syntology_url":"https://syntology.ai/paper/2309.00616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00616"}},"official":{"repos":["Pointcept/OpenIns3D"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-recognition-of-unknown-objects","slug":"unsupervised-recognition-of-unknown-objects","title":"Unsupervised Recognition of Unknown Objects for Open-World Object Detection","date":"2023-08-31","arxiv_id":"2308.16527","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":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/unsupervised-recognition-of-unknown-objects#ran","syntology_url":"https://syntology.ai/paper/2308.16527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.16527"}},"official":{"repos":["frh23333/mepu-owod"],"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/few-shot-object-detection-via-synthetic","slug":"few-shot-object-detection-via-synthetic","title":"Few-Shot Object Detection via Synthetic Features with Optimal Transport","date":"2023-08-29","arxiv_id":"2308.15005","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/few-shot-object-detection-via-synthetic#ran","syntology_url":"https://syntology.ai/paper/2308.15005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15005"}},"official":null}},{"url":"/paper/learning-to-upsample-by-learning-to-sample","slug":"learning-to-upsample-by-learning-to-sample","title":"Learning to Upsample by Learning to Sample","date":"2023-08-29","arxiv_id":"2308.15085","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/learning-to-upsample-by-learning-to-sample#ran","syntology_url":"https://syntology.ai/paper/2308.15085","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15085"}},"official":{"repos":["tiny-smart/dysample"],"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/amsp-uod-when-vortex-convolution-and","slug":"amsp-uod-when-vortex-convolution-and","title":"AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection","date":"2023-08-23","arxiv_id":"2308.11918","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"3 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/amsp-uod-when-vortex-convolution-and#ran","syntology_url":"https://syntology.ai/paper/2308.11918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11918"}},"official":{"repos":["zhoujingchun03/amsp-uod"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/villa-fine-grained-vision-language","slug":"villa-fine-grained-vision-language","title":"ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data","date":"2023-08-22","arxiv_id":"2308.11194","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"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) · 3 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/villa-fine-grained-vision-language#ran","syntology_url":"https://syntology.ai/paper/2308.11194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11194"}},"official":{"repos":["stanfordmimi/villa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dataset-quantization","slug":"dataset-quantization","title":"Dataset Quantization","date":"2023-08-21","arxiv_id":"2308.10524","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"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 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/dataset-quantization#ran","syntology_url":"https://syntology.ai/paper/2308.10524","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10524"}},"official":{"repos":["magic-research/dataset_quantization"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusiontrack-diffusion-model-for-multi","slug":"diffusiontrack-diffusion-model-for-multi","title":"DiffusionTrack: Diffusion Model For Multi-Object Tracking","date":"2023-08-19","arxiv_id":"2308.09905","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":2,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 3 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diffusiontrack-diffusion-model-for-multi#ran","syntology_url":"https://syntology.ai/paper/2308.09905","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09905"}},"official":{"repos":["rainbowluocs/diffusiontrack"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rlipv2-fast-scaling-of-relational-language","slug":"rlipv2-fast-scaling-of-relational-language","title":"RLIPv2: Fast Scaling of Relational Language-Image Pre-training","date":"2023-08-18","arxiv_id":"2308.09351","repositories_listed":3,"syntology":{"n":30,"n_ran":22,"n_constructed":5,"n_ran_checked":18,"n_instrument":4,"n_unverified":8,"n_honours":0,"n_violates":1,"n_no_contract":17,"n_pointer_only":0,"phrase":"22 ran (of which 5 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 1 violated, 17 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/rlipv2-fast-scaling-of-relational-language#ran","syntology_url":"https://syntology.ai/paper/2308.09351","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09351"}},"official":{"repos":["jacobyuan7/rlipv2"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mononerd-nerf-like-representations-for","slug":"mononerd-nerf-like-representations-for","title":"MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection","date":"2023-08-18","arxiv_id":"2308.09421","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":3,"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/mononerd-nerf-like-representations-for#ran","syntology_url":"https://syntology.ai/paper/2308.09421","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09421"}},"official":{"repos":["cskkxjk/mononerd"],"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/small-object-detection-via-coarse-to-fine","slug":"small-object-detection-via-coarse-to-fine","title":"Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning","date":"2023-08-18","arxiv_id":"2308.09534","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/small-object-detection-via-coarse-to-fine#ran","syntology_url":"https://syntology.ai/paper/2308.09534","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09534"}},"official":{"repos":["shaunyuan22/cfinet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-equilibrium-object-detection","slug":"deep-equilibrium-object-detection","title":"Deep Equilibrium Object Detection","date":"2023-08-18","arxiv_id":"2308.09564","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":7,"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/deep-equilibrium-object-detection#ran","syntology_url":"https://syntology.ai/paper/2308.09564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09564"}},"official":{"repos":["mcg-nju/deqdet"],"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/far3d-expanding-the-horizon-for-surround-view","slug":"far3d-expanding-the-horizon-for-surround-view","title":"Far3D: Expanding the Horizon for Surround-view 3D Object Detection","date":"2023-08-18","arxiv_id":"2308.09616","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":8,"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) · 0 unverified","sample_list":"/paper/far3d-expanding-the-horizon-for-surround-view#ran","syntology_url":"https://syntology.ai/paper/2308.09616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09616"}},"official":{"repos":["megvii-research/far3d"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/imgeonet-image-induced-geometry-aware-voxel","slug":"imgeonet-image-induced-geometry-aware-voxel","title":"ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection","date":"2023-08-17","arxiv_id":"2308.09098","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/imgeonet-image-induced-geometry-aware-voxel#ran","syntology_url":"https://syntology.ai/paper/2308.09098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09098"}},"official":{"repos":["ttaoREtw/ImGeoNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unitr-a-unified-and-efficient-multi-modal","slug":"unitr-a-unified-and-efficient-multi-modal","title":"UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation","date":"2023-08-15","arxiv_id":"2308.07732","repositories_listed":3,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/unitr-a-unified-and-efficient-multi-modal#ran","syntology_url":"https://syntology.ai/paper/2308.07732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07732"}},"official":{"repos":["haiyang-w/unitr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/partner-level-up-the-polar-representation-for","slug":"partner-level-up-the-polar-representation-for","title":"PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection","date":"2023-08-08","arxiv_id":"2308.03982","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/partner-level-up-the-polar-representation-for#ran","syntology_url":"https://syntology.ai/paper/2308.03982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03982"}},"official":{"repos":["fudan-zvg/partner"],"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/v-detr-detr-with-vertex-relative-position","slug":"v-detr-detr-with-vertex-relative-position","title":"V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection","date":"2023-08-08","arxiv_id":"2308.04409","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"10 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/v-detr-detr-with-vertex-relative-position#ran","syntology_url":"https://syntology.ai/paper/2308.04409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.04409"}},"official":{"repos":["yichaoshen-ms/v-detr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/featenhancer-enhancing-hierarchical-features","slug":"featenhancer-enhancing-hierarchical-features","title":"FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision","date":"2023-08-07","arxiv_id":"2308.03594","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/featenhancer-enhancing-hierarchical-features#ran","syntology_url":"https://syntology.ai/paper/2308.03594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03594"}},"official":{"repos":["khurramHashmi/FeatEnHancer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/strategic-preys-make-acute-predators","slug":"strategic-preys-make-acute-predators","title":"Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects","date":"2023-08-06","arxiv_id":"2308.03166","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":13,"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) · 5 unverified","sample_list":"/paper/strategic-preys-make-acute-predators#ran","syntology_url":"https://syntology.ai/paper/2308.03166","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03166"}},"official":{"repos":["chunminghe/camouflageator"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/etran-energy-based-transferability-estimation","slug":"etran-energy-based-transferability-estimation","title":"ETran: Energy-Based Transferability Estimation","date":"2023-08-03","arxiv_id":"2308.02027","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/etran-energy-based-transferability-estimation#ran","syntology_url":"https://syntology.ai/paper/2308.02027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.02027"}},"official":{"repos":["mgholamikn/ETran"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rcs-yolo-a-fast-and-high-accuracy-object","slug":"rcs-yolo-a-fast-and-high-accuracy-object","title":"RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection","date":"2023-07-31","arxiv_id":"2307.16412","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rcs-yolo-a-fast-and-high-accuracy-object#ran","syntology_url":"https://syntology.ai/paper/2307.16412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.16412"}},"official":{"repos":["mkang315/rcs-yolo"],"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","unlocated"]}}},{"url":"/paper/nerf-det-learning-geometry-aware-volumetric","slug":"nerf-det-learning-geometry-aware-volumetric","title":"NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection","date":"2023-07-27","arxiv_id":"2307.14620","repositories_listed":3,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"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 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/nerf-det-learning-geometry-aware-volumetric#ran","syntology_url":"https://syntology.ai/paper/2307.14620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.14620"}},"official":{"repos":["facebookresearch/nerf-det","open-mmlab/mmdetection3d"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/spatio-temporal-domain-awareness-for-multi","slug":"spatio-temporal-domain-awareness-for-multi","title":"Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception","date":"2023-07-26","arxiv_id":"2307.13929","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/spatio-temporal-domain-awareness-for-multi#ran","syntology_url":"https://syntology.ai/paper/2307.13929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.13929"}},"official":null}},{"url":"/paper/recursivedet-end-to-end-region-based","slug":"recursivedet-end-to-end-region-based","title":"RecursiveDet: End-to-End Region-based Recursive Object Detection","date":"2023-07-25","arxiv_id":"2307.13619","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":3,"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/recursivedet-end-to-end-region-based#ran","syntology_url":"https://syntology.ai/paper/2307.13619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.13619"}},"official":{"repos":["bravezzzzzz/recursivedet"],"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/prior-prototype-representation-joint-learning","slug":"prior-prototype-representation-joint-learning","title":"PRIOR: Prototype Representation Joint Learning from Medical Images and Reports","date":"2023-07-24","arxiv_id":"2307.12577","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/prior-prototype-representation-joint-learning#ran","syntology_url":"https://syntology.ai/paper/2307.12577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12577"}},"official":{"repos":["qtacierp/prior"],"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/pg-rcnn-semantic-surface-point-generation-for","slug":"pg-rcnn-semantic-surface-point-generation-for","title":"PG-RCNN: Semantic Surface Point Generation for 3D Object Detection","date":"2023-07-24","arxiv_id":"2307.12637","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"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 1 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/pg-rcnn-semantic-surface-point-generation-for#ran","syntology_url":"https://syntology.ai/paper/2307.12637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12637"}},"official":{"repos":["quotation2520/pg-rcnn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/described-object-detection-liberating-object-1","slug":"described-object-detection-liberating-object-1","title":"Described Object Detection: Liberating Object Detection with Flexible Expressions","date":"2023-07-24","arxiv_id":"2307.12813","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/described-object-detection-liberating-object-1#ran","syntology_url":"https://syntology.ai/paper/2307.12813","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12813"}},"official":{"repos":["shikras/d-cube"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dq-det-learning-dynamic-query-combinations","slug":"dq-det-learning-dynamic-query-combinations","title":"Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation","date":"2023-07-23","arxiv_id":"2307.12239","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dq-det-learning-dynamic-query-combinations#ran","syntology_url":"https://syntology.ai/paper/2307.12239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12239"}},"official":{"repos":["bytedance/dq-det"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/augmented-box-replay-overcoming-foreground","slug":"augmented-box-replay-overcoming-foreground","title":"Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection","date":"2023-07-23","arxiv_id":"2307.12427","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/augmented-box-replay-overcoming-foreground#ran","syntology_url":"https://syntology.ai/paper/2307.12427","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12427"}},"official":{"repos":["yuyangsunshine/abr_iod"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-directly-trained-spiking-neural-networks","slug":"deep-directly-trained-spiking-neural-networks","title":"Deep Directly-Trained Spiking Neural Networks for Object Detection","date":"2023-07-21","arxiv_id":"2307.11411","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 1 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/deep-directly-trained-spiking-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2307.11411","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.11411"}},"official":{"repos":["BICLab/EMS-YOLO"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/core-cooperative-reconstruction-for-multi","slug":"core-cooperative-reconstruction-for-multi","title":"CORE: Cooperative Reconstruction for Multi-Agent Perception","date":"2023-07-21","arxiv_id":"2307.11514","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/core-cooperative-reconstruction-for-multi#ran","syntology_url":"https://syntology.ai/paper/2307.11514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.11514"}},"official":{"repos":["zllxot/core"],"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/aligndet-aligning-pre-training-and-fine","slug":"aligndet-aligning-pre-training-and-fine","title":"AlignDet: Aligning Pre-training and Fine-tuning in Object Detection","date":"2023-07-20","arxiv_id":"2307.11077","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/aligndet-aligning-pre-training-and-fine#ran","syntology_url":"https://syntology.ai/paper/2307.11077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.11077"}},"official":{"repos":["liming-ai/AlignDet"],"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/domain-adaptation-for-enhanced-object","slug":"domain-adaptation-for-enhanced-object","title":"Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather","date":"2023-07-18","arxiv_id":"2307.09676","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":9,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/domain-adaptation-for-enhanced-object#ran","syntology_url":"https://syntology.ai/paper/2307.09676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09676"}},"official":{"repos":["jinlong17/da-detect"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-point-affiliation-in-feature-upsampling","slug":"on-point-affiliation-in-feature-upsampling","title":"On Point Affiliation in Feature Upsampling","date":"2023-07-17","arxiv_id":"2307.08198","repositories_listed":2,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"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) · 3 unverified","sample_list":"/paper/on-point-affiliation-in-feature-upsampling#ran","syntology_url":"https://syntology.ai/paper/2307.08198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08198"}},"official":{"repos":["tiny-smart/sapa"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/random-boxes-are-open-world-object-detectors","slug":"random-boxes-are-open-world-object-detectors","title":"Random Boxes Are Open-world Object Detectors","date":"2023-07-17","arxiv_id":"2307.08249","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":9,"n_instrument":4,"n_unverified":3,"n_honours":3,"n_violates":2,"n_no_contract":4,"n_pointer_only":16,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 2 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/random-boxes-are-open-world-object-detectors#ran","syntology_url":"https://syntology.ai/paper/2307.08249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08249"}},"official":{"repos":["scuwyh2000/randbox"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/scale-aware-modulation-meet-transformer","slug":"scale-aware-modulation-meet-transformer","title":"Scale-Aware Modulation Meet Transformer","date":"2023-07-17","arxiv_id":"2307.08579","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 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) · 2 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/scale-aware-modulation-meet-transformer#ran","syntology_url":"https://syntology.ai/paper/2307.08579","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08579"}},"official":{"repos":["afeng-x/smt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-domain-adaptive-3d-object","slug":"revisiting-domain-adaptive-3d-object","title":"Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling","date":"2023-07-16","arxiv_id":"2307.07944","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/revisiting-domain-adaptive-3d-object#ran","syntology_url":"https://syntology.ai/paper/2307.07944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.07944"}},"official":{"repos":["zhuoxiao-chen/redb-da-3ddet"],"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/patch-n-pack-navit-a-vision-transformer-for","slug":"patch-n-pack-navit-a-vision-transformer-for","title":"Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution","date":"2023-07-12","arxiv_id":"2307.06304","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/patch-n-pack-navit-a-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2307.06304","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06304"}},"official":null}},{"url":"/paper/ha-vid-a-human-assembly-video-dataset-for","slug":"ha-vid-a-human-assembly-video-dataset-for","title":"HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding","date":"2023-07-09","arxiv_id":"2307.05721","repositories_listed":1,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":12,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ha-vid-a-human-assembly-video-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2307.05721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.05721"}},"official":{"repos":["iai-hrc/ha-vid"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-open-vocabulary-universal-image-1","slug":"hierarchical-open-vocabulary-universal-image-1","title":"Hierarchical Open-vocabulary Universal Image Segmentation","date":"2023-07-03","arxiv_id":"2307.00764","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hierarchical-open-vocabulary-universal-image-1#ran","syntology_url":"https://syntology.ai/paper/2307.00764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00764"}},"official":{"repos":["berkeley-hipie/hipie"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spatial-temporal-enhanced-transformer-towards","slug":"spatial-temporal-enhanced-transformer-towards","title":"Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection","date":"2023-07-01","arxiv_id":"2307.00347","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":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) · 1 unverified","sample_list":"/paper/spatial-temporal-enhanced-transformer-towards#ran","syntology_url":"https://syntology.ai/paper/2307.00347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00347"}},"official":{"repos":["eaphan/stemd"],"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/mobilevig-graph-based-sparse-attention-for","slug":"mobilevig-graph-based-sparse-attention-for","title":"MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications","date":"2023-07-01","arxiv_id":"2307.00395","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/mobilevig-graph-based-sparse-attention-for#ran","syntology_url":"https://syntology.ai/paper/2307.00395","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00395"}},"official":{"repos":["sldgroup/mobilevig"],"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/desco-learning-object-recognition-with-rich","slug":"desco-learning-object-recognition-with-rich","title":"DesCo: Learning Object Recognition with Rich Language Descriptions","date":"2023-06-24","arxiv_id":"2306.14060","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/desco-learning-object-recognition-with-rich#ran","syntology_url":"https://syntology.ai/paper/2306.14060","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14060"}},"official":null}},{"url":"/paper/robust-semantic-segmentation-strong","slug":"robust-semantic-segmentation-strong","title":"Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models","date":"2023-06-22","arxiv_id":"2306.12941","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"8 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robust-semantic-segmentation-strong#ran","syntology_url":"https://syntology.ai/paper/2306.12941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12941"}},"official":{"repos":["nmndeep/robust-segmentation"],"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":["listed","official"]}}},{"url":"/paper/lvm-med-learning-large-scale-self-supervised-1","slug":"lvm-med-learning-large-scale-self-supervised-1","title":"LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching","date":"2023-06-20","arxiv_id":"2306.11925","repositories_listed":1,"syntology":{"n":13,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":13,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/lvm-med-learning-large-scale-self-supervised-1#ran","syntology_url":"https://syntology.ai/paper/2306.11925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11925"}},"official":{"repos":["duyhominhnguyen/LVM-Med"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/tame-a-wild-camera-in-the-wild-monocular-1","slug":"tame-a-wild-camera-in-the-wild-monocular-1","title":"Tame a Wild Camera: In-the-Wild Monocular Camera Calibration","date":"2023-06-19","arxiv_id":"2306.10988","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/tame-a-wild-camera-in-the-wild-monocular-1#ran","syntology_url":"https://syntology.ai/paper/2306.10988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10988"}},"official":{"repos":["shngjz/wildcamera"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/avoidds-aircraft-vision-based-intruder-1","slug":"avoidds-aircraft-vision-based-intruder-1","title":"AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator","date":"2023-06-19","arxiv_id":"2306.11203","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/avoidds-aircraft-vision-based-intruder-1#ran","syntology_url":"https://syntology.ai/paper/2306.11203","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11203"}},"official":{"repos":["sisl/visionbasedaircraftdaa"],"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/infinite-photorealistic-worlds-using-1","slug":"infinite-photorealistic-worlds-using-1","title":"Infinite Photorealistic Worlds using Procedural Generation","date":"2023-06-15","arxiv_id":"2306.09310","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/infinite-photorealistic-worlds-using-1#ran","syntology_url":"https://syntology.ai/paper/2306.09310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09310"}},"official":{"repos":["princeton-vl/infinigen"],"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/multiclass-confidence-and-localization-1","slug":"multiclass-confidence-and-localization-1","title":"Multiclass Confidence and Localization Calibration for Object Detection","date":"2023-06-14","arxiv_id":"2306.08271","repositories_listed":2,"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/multiclass-confidence-and-localization-1#ran","syntology_url":"https://syntology.ai/paper/2306.08271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.08271"}},"official":{"repos":["bimsarapathiraja/mccl"],"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/referring-camouflaged-object-detection","slug":"referring-camouflaged-object-detection","title":"Referring Camouflaged Object Detection","date":"2023-06-13","arxiv_id":"2306.07532","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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) · 5 unverified","sample_list":"/paper/referring-camouflaged-object-detection#ran","syntology_url":"https://syntology.ai/paper/2306.07532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07532"}},"official":{"repos":["zhangxuying1004/refcod"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-token-pruning-for-object-detection","slug":"revisiting-token-pruning-for-object-detection","title":"Revisiting Token Pruning for Object Detection and Instance Segmentation","date":"2023-06-12","arxiv_id":"2306.07050","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":0,"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/revisiting-token-pruning-for-object-detection#ran","syntology_url":"https://syntology.ai/paper/2306.07050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07050"}},"official":{"repos":["uzh-rpg/svit"],"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/detzero-rethinking-offboard-3d-object","slug":"detzero-rethinking-offboard-3d-object","title":"DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds","date":"2023-06-09","arxiv_id":"2306.06023","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":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) · 1 unverified","sample_list":"/paper/detzero-rethinking-offboard-3d-object#ran","syntology_url":"https://syntology.ai/paper/2306.06023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06023"}},"official":{"repos":["pjlab-adg/detzero"],"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/fastervit-fast-vision-transformers-with","slug":"fastervit-fast-vision-transformers-with","title":"FasterViT: Fast Vision Transformers with Hierarchical Attention","date":"2023-06-09","arxiv_id":"2306.06189","repositories_listed":2,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":9,"phrase":"4 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; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/fastervit-fast-vision-transformers-with#ran","syntology_url":"https://syntology.ai/paper/2306.06189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06189"}},"official":{"repos":["NVlabs/FasterViT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}}],"record_sha256":"77a8cb4a6d1397b8f488bab59b83d7f5d5db1c6953163446668dd14d58e0c2f5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}