{"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/panoptic-segmentation/papers/ran/1","list_of":"/task/panoptic-segmentation","task":"Panoptic Segmentation","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":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,88],"of":88,"counts":{"archive_papers_tagged":462,"with_a_code_link":257,"where_syntology_ran_a_sample":88,"not_listed_spam_title":0,"listed":462,"listed_where_code_ran":88,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":81,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":81,"listed_every_run_a_failure_of_syntologys_instrument":7,"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/panoptic-segmentation/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/scene-centric-unsupervised-panoptic","slug":"scene-centric-unsupervised-panoptic","title":"Scene-Centric Unsupervised Panoptic Segmentation","date":"2025-04-02","arxiv_id":"2504.01955","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/scene-centric-unsupervised-panoptic#ran","syntology_url":"https://syntology.ai/paper/2504.01955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.01955"}},"official":{"repos":["visinf/cups"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperseg-towards-universal-visual","slug":"hyperseg-towards-universal-visual","title":"HyperSeg: Towards Universal Visual Segmentation with Large Language Model","date":"2024-11-26","arxiv_id":"2411.17606","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":9,"n_instrument":4,"n_unverified":4,"n_honours":1,"n_violates":1,"n_no_contract":7,"n_pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/hyperseg-towards-universal-visual#ran","syntology_url":"https://syntology.ai/paper/2411.17606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.17606"}},"official":{"repos":["congvvc/HyperSeg"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/pcf-lift-panoptic-lifting-by-probabilistic","slug":"pcf-lift-panoptic-lifting-by-probabilistic","title":"PCF-Lift: Panoptic Lifting by Probabilistic Contrastive Fusion","date":"2024-10-14","arxiv_id":"2410.10659","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":0,"n_ran_checked":12,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":11,"n_pointer_only":9,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/pcf-lift-panoptic-lifting-by-probabilistic#ran","syntology_url":"https://syntology.ai/paper/2410.10659","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10659"}},"official":{"repos":["runsong123/pcf-lift"],"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":["found_in_text","official"]}}},{"url":"/paper/condition-aware-multimodal-fusion-for-robust","slug":"condition-aware-multimodal-fusion-for-robust","title":"CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes","date":"2024-10-14","arxiv_id":"2410.10791","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/condition-aware-multimodal-fusion-for-robust#ran","syntology_url":"https://syntology.ai/paper/2410.10791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10791"}},"official":{"repos":["timbroed/cafuser"],"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/task-aligned-part-aware-panoptic-segmentation-1","slug":"task-aligned-part-aware-panoptic-segmentation-1","title":"Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations","date":"2024-06-14","arxiv_id":"2406.10114","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":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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/task-aligned-part-aware-panoptic-segmentation-1#ran","syntology_url":"https://syntology.ai/paper/2406.10114","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10114"}},"official":{"repos":["tue-mps/tapps"],"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/4d-panoptic-scene-graph-generation-1","slug":"4d-panoptic-scene-graph-generation-1","title":"4D Panoptic Scene Graph Generation","date":"2024-05-16","arxiv_id":"2405.10305","repositories_listed":3,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":10,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/4d-panoptic-scene-graph-generation-1#ran","syntology_url":"https://syntology.ai/paper/2405.10305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.10305"}},"official":{"repos":["jingkang50/psg4d","Jingkang50/OpenPSG","jingkang50/openpvsg"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/building-a-strong-pre-training-baseline-for","slug":"building-a-strong-pre-training-baseline-for","title":"Building a Strong Pre-Training Baseline for Universal 3D Large-Scale Perception","date":"2024-05-12","arxiv_id":"2405.07201","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/building-a-strong-pre-training-baseline-for#ran","syntology_url":"https://syntology.ai/paper/2405.07201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07201"}},"official":{"repos":["chenhaomingbob/csc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/eclipse-efficient-continual-learning-in","slug":"eclipse-efficient-continual-learning-in","title":"ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning","date":"2024-03-29","arxiv_id":"2403.20126","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/eclipse-efficient-continual-learning-in#ran","syntology_url":"https://syntology.ai/paper/2403.20126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.20126"}},"official":{"repos":["clovaai/ECLIPSE"],"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":["official"]}}},{"url":"/paper/psalm-pixelwise-segmentation-with-large-multi","slug":"psalm-pixelwise-segmentation-with-large-multi","title":"PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model","date":"2024-03-21","arxiv_id":"2403.14598","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"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) · 4 unverified","sample_list":"/paper/psalm-pixelwise-segmentation-with-large-multi#ran","syntology_url":"https://syntology.ai/paper/2403.14598","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14598"}},"official":{"repos":["zamling/psalm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/pem-prototype-based-efficient-maskformer-for","slug":"pem-prototype-based-efficient-maskformer-for","title":"PEM: Prototype-based Efficient MaskFormer for Image Segmentation","date":"2024-02-29","arxiv_id":"2402.19422","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pem-prototype-based-efficient-maskformer-for#ran","syntology_url":"https://syntology.ai/paper/2402.19422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19422"}},"official":{"repos":["niccolocavagnero/pem"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-of-lidar-3d-point","slug":"self-supervised-learning-of-lidar-3d-point","title":"Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration","date":"2024-01-23","arxiv_id":"2401.12452","repositories_listed":2,"syntology":{"n":26,"n_ran":24,"n_constructed":0,"n_ran_checked":18,"n_instrument":6,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":17,"n_pointer_only":26,"phrase":"24 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 1 violated, 17 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-supervised-learning-of-lidar-3d-point#ran","syntology_url":"https://syntology.ai/paper/2401.12452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.12452"}},"official":{"repos":["eaphan/nclr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/muses-the-multi-sensor-semantic-perception","slug":"muses-the-multi-sensor-semantic-perception","title":"MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty","date":"2024-01-23","arxiv_id":"2401.12761","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":13,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/muses-the-multi-sensor-semantic-perception#ran","syntology_url":"https://syntology.ai/paper/2401.12761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.12761"}},"official":{"repos":["timbroed/MUSES"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-latent-diffusion-approach-for","slug":"a-simple-latent-diffusion-approach-for","title":"A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting","date":"2024-01-18","arxiv_id":"2401.10227","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-simple-latent-diffusion-approach-for#ran","syntology_url":"https://syntology.ai/paper/2401.10227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10227"}},"official":{"repos":["segments-ai/latent-diffusion-segmentation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rap-sam-towards-real-time-all-purpose-segment","slug":"rap-sam-towards-real-time-all-purpose-segment","title":"RAP-SAM: Towards Real-Time All-Purpose Segment Anything","date":"2024-01-18","arxiv_id":"2401.10228","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/rap-sam-towards-real-time-all-purpose-segment#ran","syntology_url":"https://syntology.ai/paper/2401.10228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10228"}},"official":{"repos":["xushilin1/rap-sam"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/omg-seg-is-one-model-good-enough-for-all","slug":"omg-seg-is-one-model-good-enough-for-all","title":"OMG-Seg: Is One Model Good Enough For All Segmentation?","date":"2024-01-18","arxiv_id":"2401.10229","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/omg-seg-is-one-model-good-enough-for-all#ran","syntology_url":"https://syntology.ai/paper/2401.10229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10229"}},"official":{"repos":["lxtgh/omg-seg"],"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":["official"]}}},{"url":"/paper/unsupervised-universal-image-segmentation","slug":"unsupervised-universal-image-segmentation","title":"Unsupervised Universal Image Segmentation","date":"2023-12-28","arxiv_id":"2312.17243","repositories_listed":2,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":6,"n_instrument":7,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"13 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; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-universal-image-segmentation#ran","syntology_url":"https://syntology.ai/paper/2312.17243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17243"}},"official":{"repos":["u2seg/u2seg"],"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":["listed","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/panoptic-video-scene-graph-generation-1","slug":"panoptic-video-scene-graph-generation-1","title":"Panoptic Video Scene Graph Generation","date":"2023-11-28","arxiv_id":"2311.17058","repositories_listed":3,"syntology":{"n":11,"n_ran":11,"n_constructed":2,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":3,"phrase":"11 ran (of which 2 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/panoptic-video-scene-graph-generation-1#ran","syntology_url":"https://syntology.ai/paper/2311.17058","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17058"}},"official":{"repos":["jingkang50/openpvsg","lilydaytoy/openpvsg","lilydaytoy/pvsgannotation"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":2,"n_ran_no_instrument_failure":11,"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/finite-scalar-quantization-vq-vae-made-simple","slug":"finite-scalar-quantization-vq-vae-made-simple","title":"Finite Scalar Quantization: VQ-VAE Made Simple","date":"2023-09-27","arxiv_id":"2309.15505","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 2 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/finite-scalar-quantization-vq-vae-made-simple#ran","syntology_url":"https://syntology.ai/paper/2309.15505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.15505"}},"official":{"repos":["google-research/google-research"],"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/panopticndt-efficient-and-robust-panoptic","slug":"panopticndt-efficient-and-robust-panoptic","title":"PanopticNDT: Efficient and Robust Panoptic Mapping","date":"2023-09-24","arxiv_id":"2309.13635","repositories_listed":4,"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":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) · 7 unverified","sample_list":"/paper/panopticndt-efficient-and-robust-panoptic#ran","syntology_url":"https://syntology.ai/paper/2309.13635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13635"}},"official":{"repos":["tui-nicr/panoptic-mapping"],"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/uniseg-a-unified-multi-modal-lidar","slug":"uniseg-a-unified-multi-modal-lidar","title":"UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase","date":"2023-09-11","arxiv_id":"2309.05573","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":7,"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/uniseg-a-unified-multi-modal-lidar#ran","syntology_url":"https://syntology.ai/paper/2309.05573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05573"}},"official":{"repos":["pjlab-adg/pcseg"],"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/tracking-anything-with-decoupled-video","slug":"tracking-anything-with-decoupled-video","title":"Tracking Anything with Decoupled Video Segmentation","date":"2023-09-07","arxiv_id":"2309.03903","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tracking-anything-with-decoupled-video#ran","syntology_url":"https://syntology.ai/paper/2309.03903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.03903"}},"official":{"repos":["hkchengrex/Tracking-Anything-with-DEVA"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/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/lidar-camera-panoptic-segmentation-via","slug":"lidar-camera-panoptic-segmentation-via","title":"LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment","date":"2023-08-03","arxiv_id":"2308.01686","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lidar-camera-panoptic-segmentation-via#ran","syntology_url":"https://syntology.ai/paper/2308.01686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01686"}},"official":{"repos":["zhangzw12319/lcps"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/point2mask-point-supervised-panoptic","slug":"point2mask-point-supervised-panoptic","title":"Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport","date":"2023-08-03","arxiv_id":"2308.01779","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/point2mask-point-supervised-panoptic#ran","syntology_url":"https://syntology.ai/paper/2308.01779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01779"}},"official":{"repos":["liwentomng/point2mask"],"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/towards-deeply-unified-depth-aware-panoptic","slug":"towards-deeply-unified-depth-aware-panoptic","title":"Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning","date":"2023-07-27","arxiv_id":"2307.14786","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-deeply-unified-depth-aware-panoptic#ran","syntology_url":"https://syntology.ai/paper/2307.14786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.14786"}},"official":{"repos":["jwh97nn/DeepDPS"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/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/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/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/cellvit-vision-transformers-for-precise-cell","slug":"cellvit-vision-transformers-for-precise-cell","title":"CellViT: Vision Transformers for Precise Cell Segmentation and Classification","date":"2023-06-27","arxiv_id":"2306.15350","repositories_listed":3,"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/cellvit-vision-transformers-for-precise-cell#ran","syntology_url":"https://syntology.ai/paper/2306.15350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.15350"}},"official":{"repos":["tio-ikim/cellvit"],"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/faster-segment-anything-towards-lightweight","slug":"faster-segment-anything-towards-lightweight","title":"Faster Segment Anything: Towards Lightweight SAM for Mobile Applications","date":"2023-06-25","arxiv_id":"2306.14289","repositories_listed":3,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/faster-segment-anything-towards-lightweight#ran","syntology_url":"https://syntology.ai/paper/2306.14289","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14289"}},"official":{"repos":["chaoningzhang/mobilesam"],"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/primitive-generation-and-semantic-related-1","slug":"primitive-generation-and-semantic-related-1","title":"Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation","date":"2023-06-19","arxiv_id":"2306.11087","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"9 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/primitive-generation-and-semantic-related-1#ran","syntology_url":"https://syntology.ai/paper/2306.11087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11087"}},"official":{"repos":["heshuting555/PADing"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dformer-diffusion-guided-transformer-for","slug":"dformer-diffusion-guided-transformer-for","title":"DFormer: Diffusion-guided Transformer for Universal Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03437","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dformer-diffusion-guided-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2306.03437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03437"}},"official":{"repos":["cp3wan/dformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/buol-a-bottom-up-framework-with-occupancy-1","slug":"buol-a-bottom-up-framework-with-occupancy-1","title":"BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single Image","date":"2023-06-01","arxiv_id":"2306.00965","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/buol-a-bottom-up-framework-with-occupancy-1#ran","syntology_url":"https://syntology.ai/paper/2306.00965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00965"}},"official":{"repos":["chtsy/buol"],"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/edaps-enhanced-domain-adaptive-panoptic","slug":"edaps-enhanced-domain-adaptive-panoptic","title":"EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation","date":"2023-04-27","arxiv_id":"2304.14291","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/edaps-enhanced-domain-adaptive-panoptic#ran","syntology_url":"https://syntology.ai/paper/2304.14291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.14291"}},"official":{"repos":["susaha/edaps"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/instance-neural-radiance-field","slug":"instance-neural-radiance-field","title":"Instance Neural Radiance Field","date":"2023-04-10","arxiv_id":"2304.04395","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/instance-neural-radiance-field#ran","syntology_url":"https://syntology.ai/paper/2304.04395","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04395"}},"official":{"repos":["lyclyc52/instance_nerf"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/seggpt-segmenting-everything-in-context","slug":"seggpt-segmenting-everything-in-context","title":"SegGPT: Segmenting Everything In Context","date":"2023-04-06","arxiv_id":"2304.03284","repositories_listed":3,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/seggpt-segmenting-everything-in-context#ran","syntology_url":"https://syntology.ai/paper/2304.03284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03284"}},"official":{"repos":["baaivision/painter"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/you-only-segment-once-towards-real-time","slug":"you-only-segment-once-towards-real-time","title":"You Only Segment Once: Towards Real-Time Panoptic Segmentation","date":"2023-03-26","arxiv_id":"2303.14651","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/you-only-segment-once-towards-real-time#ran","syntology_url":"https://syntology.ai/paper/2303.14651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.14651"}},"official":{"repos":["hujiecpp/yoso"],"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/tarvis-a-unified-approach-for-target-based","slug":"tarvis-a-unified-approach-for-target-based","title":"TarViS: A Unified Approach for Target-based Video Segmentation","date":"2023-01-06","arxiv_id":"2301.02657","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":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) · 2 unverified","sample_list":"/paper/tarvis-a-unified-approach-for-target-based#ran","syntology_url":"https://syntology.ai/paper/2301.02657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.02657"}},"official":{"repos":["Ali2500/TarViS"],"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/flexivit-one-model-for-all-patch-sizes","slug":"flexivit-one-model-for-all-patch-sizes","title":"FlexiViT: One Model for All Patch Sizes","date":"2022-12-15","arxiv_id":"2212.08013","repositories_listed":6,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/flexivit-one-model-for-all-patch-sizes#ran","syntology_url":"https://syntology.ai/paper/2212.08013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08013"}},"official":{"repos":["google-research/big_vision"],"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/oneformer-one-transformer-to-rule-universal","slug":"oneformer-one-transformer-to-rule-universal","title":"OneFormer: One Transformer to Rule Universal Image Segmentation","date":"2022-11-10","arxiv_id":"2211.06220","repositories_listed":4,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/oneformer-one-transformer-to-rule-universal#ran","syntology_url":"https://syntology.ai/paper/2211.06220","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.06220"}},"official":{"repos":["SHI-Labs/OneFormer"],"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/pointly-supervised-panoptic-segmentation","slug":"pointly-supervised-panoptic-segmentation","title":"Pointly-Supervised Panoptic Segmentation","date":"2022-10-25","arxiv_id":"2210.13950","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pointly-supervised-panoptic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2210.13950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13950"}},"official":{"repos":["bravegroup/psps"],"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/expediting-large-scale-vision-transformer-for","slug":"expediting-large-scale-vision-transformer-for","title":"Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning","date":"2022-10-03","arxiv_id":"2210.01035","repositories_listed":4,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/expediting-large-scale-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2210.01035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01035"}},"official":{"repos":["Expedit-LargeScale-Vision-Transformer/Expedit-DINO","Expedit-LargeScale-Vision-Transformer/Expedit-DPT","Expedit-LargeScale-Vision-Transformer/Expedit-Segmenter"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/4d-stop-panoptic-segmentation-of-4d-lidar","slug":"4d-stop-panoptic-segmentation-of-4d-lidar","title":"4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation","date":"2022-09-29","arxiv_id":"2209.14858","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/4d-stop-panoptic-segmentation-of-4d-lidar#ran","syntology_url":"https://syntology.ai/paper/2209.14858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.14858"}},"official":{"repos":["larskreuzberg/4d-stop"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/open-vocabulary-panoptic-segmentation-with","slug":"open-vocabulary-panoptic-segmentation-with","title":"Open-Vocabulary Universal Image Segmentation with MaskCLIP","date":"2022-08-18","arxiv_id":"2208.08984","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/open-vocabulary-panoptic-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/2208.08984","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08984"}},"official":{"repos":["mlpc-ucsd/maskclip"],"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/ppmn-pixel-phrase-matching-network-for-one","slug":"ppmn-pixel-phrase-matching-network-for-one","title":"PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding","date":"2022-08-11","arxiv_id":"2208.05647","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":6,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/ppmn-pixel-phrase-matching-network-for-one#ran","syntology_url":"https://syntology.ai/paper/2208.05647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05647"}},"official":{"repos":["dzh19990407/ppmn"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/cmt-deeplab-clustering-mask-transformers-for-1","slug":"cmt-deeplab-clustering-mask-transformers-for-1","title":"CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation","date":"2022-06-17","arxiv_id":"2206.08948","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/cmt-deeplab-clustering-mask-transformers-for-1#ran","syntology_url":"https://syntology.ai/paper/2206.08948","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.08948"}},"official":null}},{"url":"/paper/mask-dino-towards-a-unified-transformer-based-1","slug":"mask-dino-towards-a-unified-transformer-based-1","title":"Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation","date":"2022-06-06","arxiv_id":"2206.02777","repositories_listed":10,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":3,"n_instrument":8,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 8 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mask-dino-towards-a-unified-transformer-based-1#ran","syntology_url":"https://syntology.ai/paper/2206.02777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.02777"}},"official":{"repos":["idea-research/maskdino"],"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/panopticdepth-a-unified-framework-for-depth","slug":"panopticdepth-a-unified-framework-for-depth","title":"PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation","date":"2022-06-01","arxiv_id":"2206.00468","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"phrase":"7 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/panopticdepth-a-unified-framework-for-depth#ran","syntology_url":"https://syntology.ai/paper/2206.00468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.00468"}},"official":{"repos":["naiyugao/panopticdepth"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/uvim-a-unified-modeling-approach-for-vision","slug":"uvim-a-unified-modeling-approach-for-vision","title":"UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes","date":"2022-05-20","arxiv_id":"2205.10337","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":0,"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/uvim-a-unified-modeling-approach-for-vision#ran","syntology_url":"https://syntology.ai/paper/2205.10337","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10337"}},"official":{"repos":["google-research/big_vision"],"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/joint-forecasting-of-panoptic-segmentations","slug":"joint-forecasting-of-panoptic-segmentations","title":"Joint Forecasting of Panoptic Segmentations with Difference Attention","date":"2022-04-14","arxiv_id":"2204.07157","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/joint-forecasting-of-panoptic-segmentations#ran","syntology_url":"https://syntology.ai/paper/2204.07157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07157"}},"official":{"repos":["cgraber/psf-diffattn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/focal-modulation-networks","slug":"focal-modulation-networks","title":"Focal Modulation Networks","date":"2022-03-22","arxiv_id":"2203.11926","repositories_listed":9,"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/focal-modulation-networks#ran","syntology_url":"https://syntology.ai/paper/2203.11926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11926"}},"official":{"repos":["microsoft/FocalNet"],"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/lidar-based-4d-panoptic-segmentation-via","slug":"lidar-based-4d-panoptic-segmentation-via","title":"LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Network","date":"2022-03-14","arxiv_id":"2203.07186","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/lidar-based-4d-panoptic-segmentation-via#ran","syntology_url":"https://syntology.ai/paper/2203.07186","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07186"}},"official":{"repos":["hongfz16/DS-Net"],"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/mlseg-image-and-video-segmentation-as-multi","slug":"mlseg-image-and-video-segmentation-as-multi","title":"RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation","date":"2022-03-08","arxiv_id":"2203.04187","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"9 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mlseg-image-and-video-segmentation-as-multi#ran","syntology_url":"https://syntology.ai/paper/2203.04187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04187"}},"official":{"repos":["openseg-group/mlseg","openseg-group/rankseg"],"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":["found_in_text","official"]}}},{"url":"/paper/hoi4d-a-4d-egocentric-dataset-for-category","slug":"hoi4d-a-4d-egocentric-dataset-for-category","title":"HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction","date":"2022-03-03","arxiv_id":"2203.01577","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/hoi4d-a-4d-egocentric-dataset-for-category#ran","syntology_url":"https://syntology.ai/paper/2203.01577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01577"}},"official":{"repos":["leolyliu/HOI4D-Instructions"],"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/visual-attention-network","slug":"visual-attention-network","title":"Visual Attention Network","date":"2022-02-20","arxiv_id":"2202.09741","repositories_listed":21,"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/visual-attention-network#ran","syntology_url":"https://syntology.ai/paper/2202.09741","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.09741"}},"official":{"repos":["Visual-Attention-Network/VAN-Classification"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mseg-a-composite-dataset-for-multi-domain-1","slug":"mseg-a-composite-dataset-for-multi-domain-1","title":"MSeg: A Composite Dataset for Multi-domain Semantic Segmentation","date":"2021-12-27","arxiv_id":"2112.13762","repositories_listed":2,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"phrase":"14 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mseg-a-composite-dataset-for-multi-domain-1#ran","syntology_url":"https://syntology.ai/paper/2112.13762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13762"}},"official":{"repos":["mseg-dataset/mseg-semantic"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mask2former-for-video-instance-segmentation","slug":"mask2former-for-video-instance-segmentation","title":"Mask2Former for Video Instance Segmentation","date":"2021-12-20","arxiv_id":"2112.10764","repositories_listed":6,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mask2former-for-video-instance-segmentation#ran","syntology_url":"https://syntology.ai/paper/2112.10764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.10764"}},"official":{"repos":["facebookresearch/Mask2Former"],"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/masked-attention-mask-transformer-for","slug":"masked-attention-mask-transformer-for","title":"Masked-attention Mask Transformer for Universal Image Segmentation","date":"2021-12-02","arxiv_id":"2112.01527","repositories_listed":7,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/masked-attention-mask-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2112.01527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.01527"}},"official":{"repos":["facebookresearch/Mask2Former"],"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/pnp-detr-towards-efficient-visual-analysis","slug":"pnp-detr-towards-efficient-visual-analysis","title":"PnP-DETR: Towards Efficient Visual Analysis with Transformers","date":"2021-09-15","arxiv_id":"2109.07036","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/pnp-detr-towards-efficient-visual-analysis#ran","syntology_url":"https://syntology.ai/paper/2109.07036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.07036"}},"official":{"repos":["twangnh/pnp-detr"],"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/cylindrical-and-asymmetrical-3d-convolution-1","slug":"cylindrical-and-asymmetrical-3d-convolution-1","title":"Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception","date":"2021-09-12","arxiv_id":"2109.05441","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/cylindrical-and-asymmetrical-3d-convolution-1#ran","syntology_url":"https://syntology.ai/paper/2109.05441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05441"}},"official":{"repos":["xinge008/Cylinder3D"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fully-convolutional-networks-for-panoptic-1","slug":"fully-convolutional-networks-for-panoptic-1","title":"Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision","date":"2021-08-17","arxiv_id":"2108.07682","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":3,"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/fully-convolutional-networks-for-panoptic-1#ran","syntology_url":"https://syntology.ai/paper/2108.07682","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07682"}},"official":{"repos":["dvlab-research/panopticfcn"],"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/panoptic-segmentation-of-satellite-image-time","slug":"panoptic-segmentation-of-satellite-image-time","title":"Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks","date":"2021-07-16","arxiv_id":"2107.07933","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":10,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 10 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) · 1 unverified; every one of the 10 samples that ran constructed an object rather than computing a result","sample_list":"/paper/panoptic-segmentation-of-satellite-image-time#ran","syntology_url":"https://syntology.ai/paper/2107.07933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07933"}},"official":{"repos":["VSainteuf/utae-paps"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":10,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/k-net-towards-unified-image-segmentation","slug":"k-net-towards-unified-image-segmentation","title":"K-Net: Towards Unified Image Segmentation","date":"2021-06-28","arxiv_id":"2106.14855","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/k-net-towards-unified-image-segmentation#ran","syntology_url":"https://syntology.ai/paper/2106.14855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.14855"}},"official":{"repos":["zwwwayne/k-net"],"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/part-aware-panoptic-segmentation","slug":"part-aware-panoptic-segmentation","title":"Part-aware Panoptic Segmentation","date":"2021-06-11","arxiv_id":"2106.06351","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/part-aware-panoptic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2106.06351","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06351"}},"official":{"repos":["tue-mps/panoptic_parts"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/boundary-iou-improving-object-centric-image","slug":"boundary-iou-improving-object-centric-image","title":"Boundary IoU: Improving Object-Centric Image Segmentation Evaluation","date":"2021-03-30","arxiv_id":"2103.16562","repositories_listed":2,"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/boundary-iou-improving-object-centric-image#ran","syntology_url":"https://syntology.ai/paper/2103.16562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16562"}},"official":{"repos":["bowenc0221/boundary-iou-api"],"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/panoptic-polarnet-proposal-free-lidar-point","slug":"panoptic-polarnet-proposal-free-lidar-point","title":"Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation","date":"2021-03-27","arxiv_id":"2103.14962","repositories_listed":2,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/panoptic-polarnet-proposal-free-lidar-point#ran","syntology_url":"https://syntology.ai/paper/2103.14962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14962"}},"official":{"repos":["edwardzhou130/Panoptic-PolarNet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/instancerefer-cooperative-holistic","slug":"instancerefer-cooperative-holistic","title":"InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring","date":"2021-03-01","arxiv_id":"2103.01128","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/instancerefer-cooperative-holistic#ran","syntology_url":"https://syntology.ai/paper/2103.01128","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.01128"}},"official":{"repos":["CurryYuan/InstanceRefer"],"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/vip-deeplab-learning-visual-perception-with","slug":"vip-deeplab-learning-visual-perception-with","title":"ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation","date":"2020-12-09","arxiv_id":"2012.05258","repositories_listed":2,"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/vip-deeplab-learning-visual-perception-with#ran","syntology_url":"https://syntology.ai/paper/2012.05258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.05258"}},"official":{"repos":["joe-siyuan-qiao/ViP-DeepLab"],"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/lidar-based-panoptic-segmentation-via-dynamic","slug":"lidar-based-panoptic-segmentation-via-dynamic","title":"LiDAR-based Panoptic Segmentation via Dynamic Shifting Network","date":"2020-11-24","arxiv_id":"2011.11964","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/lidar-based-panoptic-segmentation-via-dynamic#ran","syntology_url":"https://syntology.ai/paper/2011.11964","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.11964"}},"official":{"repos":["hongfz16/DS-Net"],"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/towards-efficient-scene-understanding-via","slug":"towards-efficient-scene-understanding-via","title":"Towards Efficient Scene Understanding via Squeeze Reasoning","date":"2020-11-06","arxiv_id":"2011.03308","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/towards-efficient-scene-understanding-via#ran","syntology_url":"https://syntology.ai/paper/2011.03308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.03308"}},"official":{"repos":["lxtGH/SFSegNets"],"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","unlocated"]}}},{"url":"/paper/detectors-detecting-objects-with-recursive-1","slug":"detectors-detecting-objects-with-recursive-1","title":"DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution","date":"2020-06-03","arxiv_id":"2006.02334","repositories_listed":6,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/detectors-detecting-objects-with-recursive-1#ran","syntology_url":"https://syntology.ai/paper/2006.02334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.02334"}},"official":{"repos":["joe-siyuan-qiao/DetectoRS"],"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/end-to-end-object-detection-with-transformers","slug":"end-to-end-object-detection-with-transformers","title":"End-to-End Object Detection with Transformers","date":"2020-05-26","arxiv_id":"2005.12872","repositories_listed":37,"syntology":{"n":92,"n_ran":70,"n_constructed":45,"n_ran_checked":62,"n_instrument":8,"n_unverified":22,"n_honours":2,"n_violates":1,"n_no_contract":59,"n_pointer_only":19,"phrase":"70 ran (of which 45 constructed an object rather than computing a result; 62 with no instrument failure: 2 honoured, 1 violated, 59 with no contract checked; 8 where Syntology's instrument failed) · 22 unverified","sample_list":"/paper/end-to-end-object-detection-with-transformers#ran","syntology_url":"https://syntology.ai/paper/2005.12872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.12872"}},"official":{"repos":["facebookresearch/detr"],"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":["listed","official"]}}},{"url":"/paper/hierarchical-multi-scale-attention-for","slug":"hierarchical-multi-scale-attention-for","title":"Hierarchical Multi-Scale Attention for Semantic Segmentation","date":"2020-05-21","arxiv_id":"2005.10821","repositories_listed":8,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hierarchical-multi-scale-attention-for#ran","syntology_url":"https://syntology.ai/paper/2005.10821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10821"}},"official":{"repos":["NVIDIA/semantic-segmentation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/resnest-split-attention-networks","slug":"resnest-split-attention-networks","title":"ResNeSt: Split-Attention Networks","date":"2020-04-19","arxiv_id":"2004.08955","repositories_listed":36,"syntology":{"n":48,"n_ran":28,"n_constructed":0,"n_ran_checked":25,"n_instrument":3,"n_unverified":20,"n_honours":0,"n_violates":0,"n_no_contract":25,"n_pointer_only":23,"phrase":"28 ran (of which 0 constructed an object rather than computing a result; 25 with no instrument failure: 0 honoured, 0 violated, 25 with no contract checked; 3 where Syntology's instrument failed) · 20 unverified","sample_list":"/paper/resnest-split-attention-networks#ran","syntology_url":"https://syntology.ai/paper/2004.08955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.08955"}},"official":{"repos":["zhanghang1989/ResNeSt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/cityscapes-panoptic-parts-and-pascal-panoptic","slug":"cityscapes-panoptic-parts-and-pascal-panoptic","title":"Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding","date":"2020-04-16","arxiv_id":"2004.07944","repositories_listed":4,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cityscapes-panoptic-parts-and-pascal-panoptic#ran","syntology_url":"https://syntology.ai/paper/2004.07944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07944"}},"official":{"repos":["tue-mps/panoptic_parts"],"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/pointgroup-dual-set-point-grouping-for-3d","slug":"pointgroup-dual-set-point-grouping-for-3d","title":"PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation","date":"2020-04-03","arxiv_id":"2004.01658","repositories_listed":4,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pointgroup-dual-set-point-grouping-for-3d#ran","syntology_url":"https://syntology.ai/paper/2004.01658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.01658"}},"official":null}},{"url":"/paper/solov2-dynamic-faster-and-stronger","slug":"solov2-dynamic-faster-and-stronger","title":"SOLOv2: Dynamic and Fast Instance Segmentation","date":"2020-03-23","arxiv_id":"2003.10152","repositories_listed":18,"syntology":{"n":38,"n_ran":23,"n_constructed":4,"n_ran_checked":15,"n_instrument":8,"n_unverified":15,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":24,"phrase":"23 ran (of which 4 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 8 where Syntology's instrument failed) · 15 unverified","sample_list":"/paper/solov2-dynamic-faster-and-stronger#ran","syntology_url":"https://syntology.ai/paper/2003.10152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.10152"}},"official":{"repos":["WXinlong/SOLO"],"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/axial-deeplab-stand-alone-axial-attention-for","slug":"axial-deeplab-stand-alone-axial-attention-for","title":"Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation","date":"2020-03-17","arxiv_id":"2003.07853","repositories_listed":5,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/axial-deeplab-stand-alone-axial-attention-for#ran","syntology_url":"https://syntology.ai/paper/2003.07853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.07853"}},"official":{"repos":["google-research/deeplab2"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["named_in_paper"]}}},{"url":"/paper/panoptic-deeplab-a-simple-strong-and-fast","slug":"panoptic-deeplab-a-simple-strong-and-fast","title":"Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation","date":"2019-11-22","arxiv_id":"1911.10194","repositories_listed":9,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/panoptic-deeplab-a-simple-strong-and-fast#ran","syntology_url":"https://syntology.ai/paper/1911.10194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.10194"}},"official":{"repos":["bowenc0221/panoptic-deeplab"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/centermask-real-time-anchor-free-instance-1","slug":"centermask-real-time-anchor-free-instance-1","title":"CenterMask : Real-Time Anchor-Free Instance Segmentation","date":"2019-11-15","arxiv_id":"1911.06667","repositories_listed":8,"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":2,"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/centermask-real-time-anchor-free-instance-1#ran","syntology_url":"https://syntology.ai/paper/1911.06667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06667"}},"official":{"repos":["youngwanLEE/CenterMask"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/seamless-scene-segmentation","slug":"seamless-scene-segmentation","title":"Seamless Scene Segmentation","date":"2019-05-03","arxiv_id":"1905.01220","repositories_listed":5,"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/seamless-scene-segmentation#ran","syntology_url":"https://syntology.ai/paper/1905.01220","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.01220"}},"official":null}},{"url":"/paper/upsnet-a-unified-panoptic-segmentation","slug":"upsnet-a-unified-panoptic-segmentation","title":"UPSNet: A Unified Panoptic Segmentation Network","date":"2019-01-12","arxiv_id":"1901.03784","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/upsnet-a-unified-panoptic-segmentation#ran","syntology_url":"https://syntology.ai/paper/1901.03784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.03784"}},"official":{"repos":["uber-research/UPSNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/panoptic-feature-pyramid-networks","slug":"panoptic-feature-pyramid-networks","title":"Panoptic Feature Pyramid Networks","date":"2019-01-08","arxiv_id":"1901.02446","repositories_listed":12,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":4,"n_instrument":7,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"11 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; 7 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/panoptic-feature-pyramid-networks#ran","syntology_url":"https://syntology.ai/paper/1901.02446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.02446"}},"official":{"repos":["facebookresearch/detectron2"],"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/bdd100k-a-diverse-driving-video-database-with","slug":"bdd100k-a-diverse-driving-video-database-with","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","date":"2018-05-12","arxiv_id":"1805.04687","repositories_listed":4,"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/bdd100k-a-diverse-driving-video-database-with#ran","syntology_url":"https://syntology.ai/paper/1805.04687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.04687"}},"official":{"repos":["bdd100k/bdd100k","SysCV/bdd100k-models"],"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/espnet-efficient-spatial-pyramid-of-dilated","slug":"espnet-efficient-spatial-pyramid-of-dilated","title":"ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation","date":"2018-03-19","arxiv_id":"1803.06815","repositories_listed":8,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/espnet-efficient-spatial-pyramid-of-dilated#ran","syntology_url":"https://syntology.ai/paper/1803.06815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.06815"}},"official":{"repos":["sacmehta/ESPNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/mask-r-cnn","slug":"mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","arxiv_id":"1703.06870","repositories_listed":179,"syntology":{"n":140,"n_ran":101,"n_constructed":3,"n_ran_checked":90,"n_instrument":11,"n_unverified":39,"n_honours":0,"n_violates":0,"n_no_contract":90,"n_pointer_only":32,"phrase":"101 ran (of which 3 constructed an object rather than computing a result; 90 with no instrument failure: 0 honoured, 0 violated, 90 with no contract checked; 11 where Syntology's instrument failed) · 39 unverified","sample_list":"/paper/mask-r-cnn#ran","syntology_url":"https://syntology.ai/paper/1703.06870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06870"}},"official":null}}],"record_sha256":"c8c71b239637a02eef5a8992dbb16beb76144125294a22cc1ae7f1b34b9c23cd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}