{"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/instance-segmentation/papers/ran/2","list_of":"/task/instance-segmentation","task":"Instance 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":2,"pages_in_order":4,"rows_per_page":100,"rows":[101,200],"of":324,"counts":{"archive_papers_tagged":2262,"with_a_code_link":1158,"where_syntology_ran_a_sample":324,"not_listed_spam_title":0,"listed":2262,"listed_where_code_ran":324,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":289,"every_run_a_failure_of_syntologys_instrument":35,"listed_with_a_run_with_no_instrument_failure":289,"listed_every_run_a_failure_of_syntologys_instrument":35,"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/instance-segmentation/papers/ran/1","prev":"/task/instance-segmentation/papers/ran/1","next":"/task/instance-segmentation/papers/ran/3","papers":[{"url":"/paper/making-vision-transformers-efficient-from-a","slug":"making-vision-transformers-efficient-from-a","title":"Making Vision Transformers Efficient from A Token Sparsification View","date":"2023-03-15","arxiv_id":"2303.08685","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/making-vision-transformers-efficient-from-a#ran","syntology_url":"https://syntology.ai/paper/2303.08685","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08685"}},"official":{"repos":["changsn/STViT-R"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/three-guidelines-you-should-know-for","slug":"three-guidelines-you-should-know-for","title":"Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning","date":"2023-03-13","arxiv_id":"2303.06870","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/three-guidelines-you-should-know-for#ran","syntology_url":"https://syntology.ai/paper/2303.06870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06870"}},"official":{"repos":["megvii-research/us3l-cvpr2023"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/universal-instance-perception-as-object","slug":"universal-instance-perception-as-object","title":"Universal Instance Perception as Object Discovery and Retrieval","date":"2023-03-12","arxiv_id":"2303.06674","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/universal-instance-perception-as-object#ran","syntology_url":"https://syntology.ai/paper/2303.06674","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06674"}},"official":{"repos":["MasterBin-IIAU/UNINEXT"],"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/isbnet-a-3d-point-cloud-instance-segmentation","slug":"isbnet-a-3d-point-cloud-instance-segmentation","title":"ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution","date":"2023-03-01","arxiv_id":"2303.00246","repositories_listed":2,"syntology":{"n":19,"n_ran":16,"n_constructed":0,"n_ran_checked":14,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":2,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/isbnet-a-3d-point-cloud-instance-segmentation#ran","syntology_url":"https://syntology.ai/paper/2303.00246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.00246"}},"official":{"repos":["VinAIResearch/ISBNet"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/cfnet-cascade-fusion-network-for-dense","slug":"cfnet-cascade-fusion-network-for-dense","title":"CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction","date":"2023-02-13","arxiv_id":"2302.06052","repositories_listed":2,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":12,"phrase":"10 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cfnet-cascade-fusion-network-for-dense#ran","syntology_url":"https://syntology.ai/paper/2302.06052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06052"}},"official":{"repos":["zhanggang001/cednet","zhanggang001/cfnet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-layer-retrospective-retrieving-via","slug":"cross-layer-retrospective-retrieving-via","title":"Cross-Layer Retrospective Retrieving via Layer Attention","date":"2023-02-08","arxiv_id":"2302.03985","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/cross-layer-retrospective-retrieving-via#ran","syntology_url":"https://syntology.ai/paper/2302.03985","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.03985"}},"official":{"repos":["joyfang1106/mrla"],"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/patchdct-patch-refinement-for-high-quality","slug":"patchdct-patch-refinement-for-high-quality","title":"PatchDCT: Patch Refinement for High Quality Instance Segmentation","date":"2023-02-06","arxiv_id":"2302.02693","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/patchdct-patch-refinement-for-high-quality#ran","syntology_url":"https://syntology.ai/paper/2302.02693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.02693"}},"official":{"repos":["olivia-w12/patchdct"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/designing-bert-for-convolutional-networks","slug":"designing-bert-for-convolutional-networks","title":"Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling","date":"2023-01-09","arxiv_id":"2301.03580","repositories_listed":2,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":2,"phrase":"13 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/designing-bert-for-convolutional-networks#ran","syntology_url":"https://syntology.ai/paper/2301.03580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.03580"}},"official":{"repos":["keyu-tian/spark"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"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/which-pixel-to-annotate-a-label-efficient","slug":"which-pixel-to-annotate-a-label-efficient","title":"Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework","date":"2022-12-20","arxiv_id":"2212.10305","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/which-pixel-to-annotate-a-label-efficient#ran","syntology_url":"https://syntology.ai/paper/2212.10305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.10305"}},"official":{"repos":["lhaof/nuseg"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-object-localization-observing","slug":"unsupervised-object-localization-observing","title":"Unsupervised Object Localization: Observing the Background to Discover Objects","date":"2022-12-15","arxiv_id":"2212.07834","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":3,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-object-localization-observing#ran","syntology_url":"https://syntology.ai/paper/2212.07834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07834"}},"official":{"repos":["valeoai/found"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rtmdet-an-empirical-study-of-designing-real","slug":"rtmdet-an-empirical-study-of-designing-real","title":"RTMDet: An Empirical Study of Designing Real-Time Object Detectors","date":"2022-12-14","arxiv_id":"2212.07784","repositories_listed":14,"syntology":{"n":20,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":3,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/rtmdet-an-empirical-study-of-designing-real#ran","syntology_url":"https://syntology.ai/paper/2212.07784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07784"}},"official":{"repos":["open-mmlab/mmdetection"],"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/robust-perception-through-equivariance","slug":"robust-perception-through-equivariance","title":"Robust Perception through Equivariance","date":"2022-12-12","arxiv_id":"2212.06079","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/robust-perception-through-equivariance#ran","syntology_url":"https://syntology.ai/paper/2212.06079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.06079"}},"official":{"repos":["cvlab-columbia/equi4rob"],"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/mediar-harmony-of-data-centric-and-model","slug":"mediar-harmony-of-data-centric-and-model","title":"MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy","date":"2022-12-07","arxiv_id":"2212.03465","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":4,"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 4 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","sample_list":"/paper/mediar-harmony-of-data-centric-and-model#ran","syntology_url":"https://syntology.ai/paper/2212.03465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.03465"}},"official":{"repos":["lee-gihun/mediar"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/x-paste-revisit-copy-paste-at-scale-with-clip","slug":"x-paste-revisit-copy-paste-at-scale-with-clip","title":"X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion","date":"2022-12-07","arxiv_id":"2212.03863","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/x-paste-revisit-copy-paste-at-scale-with-clip#ran","syntology_url":"https://syntology.ai/paper/2212.03863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.03863"}},"official":{"repos":["yoctta/xpaste"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusioninst-diffusion-model-for-instance","slug":"diffusioninst-diffusion-model-for-instance","title":"DiffusionInst: Diffusion Model for Instance Segmentation","date":"2022-12-06","arxiv_id":"2212.02773","repositories_listed":2,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":7,"n_instrument":5,"n_unverified":4,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/diffusioninst-diffusion-model-for-instance#ran","syntology_url":"https://syntology.ai/paper/2212.02773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.02773"}},"official":{"repos":["chenhaoxing/DiffusionInst"],"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/eurnet-efficient-multi-range-relational","slug":"eurnet-efficient-multi-range-relational","title":"EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data","date":"2022-11-23","arxiv_id":"2211.12941","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"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) · 4 unverified","sample_list":"/paper/eurnet-efficient-multi-range-relational#ran","syntology_url":"https://syntology.ai/paper/2211.12941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.12941"}},"official":{"repos":["hirl-team/eurnet-image"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/detrs-with-collaborative-hybrid-assignments","slug":"detrs-with-collaborative-hybrid-assignments","title":"DETRs with Collaborative Hybrid Assignments Training","date":"2022-11-22","arxiv_id":"2211.12860","repositories_listed":6,"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/detrs-with-collaborative-hybrid-assignments#ran","syntology_url":"https://syntology.ai/paper/2211.12860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.12860"}},"official":null}},{"url":"/paper/eva-exploring-the-limits-of-masked-visual","slug":"eva-exploring-the-limits-of-masked-visual","title":"EVA: Exploring the Limits of Masked Visual Representation Learning at Scale","date":"2022-11-14","arxiv_id":"2211.07636","repositories_listed":6,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/eva-exploring-the-limits-of-masked-visual#ran","syntology_url":"https://syntology.ai/paper/2211.07636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.07636"}},"official":{"repos":["baaivision/eva","rwightman/pytorch-image-models"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/internimage-exploring-large-scale-vision","slug":"internimage-exploring-large-scale-vision","title":"InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions","date":"2022-11-10","arxiv_id":"2211.05778","repositories_listed":3,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/internimage-exploring-large-scale-vision#ran","syntology_url":"https://syntology.ai/paper/2211.05778","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.05778"}},"official":{"repos":["opengvlab/internimage"],"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/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/efficient-multi-order-gated-aggregation","slug":"efficient-multi-order-gated-aggregation","title":"MogaNet: Multi-order Gated Aggregation Network","date":"2022-11-07","arxiv_id":"2211.03295","repositories_listed":7,"syntology":{"n":15,"n_ran":12,"n_constructed":7,"n_ran_checked":10,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"12 ran (of which 7 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/efficient-multi-order-gated-aggregation#ran","syntology_url":"https://syntology.ai/paper/2211.03295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.03295"}},"official":{"repos":["Westlake-AI/MogaNet","Westlake-AI/openmixup","chengtan9907/OpenSTL"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":7,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-via-maximum-entropy","slug":"self-supervised-learning-via-maximum-entropy","title":"Self-Supervised Learning via Maximum Entropy Coding","date":"2022-10-20","arxiv_id":"2210.11464","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-supervised-learning-via-maximum-entropy#ran","syntology_url":"https://syntology.ai/paper/2210.11464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11464"}},"official":{"repos":["xinliu20/mec"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/h2rbox-horizonal-box-annotation-is-all-you","slug":"h2rbox-horizonal-box-annotation-is-all-you","title":"H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection","date":"2022-10-13","arxiv_id":"2210.06742","repositories_listed":3,"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/h2rbox-horizonal-box-annotation-is-all-you#ran","syntology_url":"https://syntology.ai/paper/2210.06742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06742"}},"official":{"repos":["yangxue0827/h2rbox-jittor","yangxue0827/h2rbox-mmrotate"],"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/latency-aware-spatial-wise-dynamic-networks","slug":"latency-aware-spatial-wise-dynamic-networks","title":"Latency-aware Spatial-wise Dynamic Networks","date":"2022-10-12","arxiv_id":"2210.06223","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":2,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/latency-aware-spatial-wise-dynamic-networks#ran","syntology_url":"https://syntology.ai/paper/2210.06223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06223"}},"official":{"repos":["leaplabthu/lasnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/ogc-unsupervised-3d-object-segmentation-from","slug":"ogc-unsupervised-3d-object-segmentation-from","title":"OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds","date":"2022-10-10","arxiv_id":"2210.04458","repositories_listed":1,"syntology":{"n":20,"n_ran":13,"n_constructed":9,"n_ran_checked":12,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":20,"phrase":"13 ran (of which 9 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/ogc-unsupervised-3d-object-segmentation-from#ran","syntology_url":"https://syntology.ai/paper/2210.04458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04458"}},"official":{"repos":["vlar-group/ogc"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":9,"n_ran_no_instrument_failure":12,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/what-the-daam-interpreting-stable-diffusion","slug":"what-the-daam-interpreting-stable-diffusion","title":"What the DAAM: Interpreting Stable Diffusion Using Cross Attention","date":"2022-10-10","arxiv_id":"2210.04885","repositories_listed":2,"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/what-the-daam-interpreting-stable-diffusion#ran","syntology_url":"https://syntology.ai/paper/2210.04885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04885"}},"official":{"repos":["castorini/daam"],"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/humans-need-not-label-more-humans-occlusion","slug":"humans-need-not-label-more-humans-occlusion","title":"Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation","date":"2022-10-07","arxiv_id":"2210.03686","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/humans-need-not-label-more-humans-occlusion#ran","syntology_url":"https://syntology.ai/paper/2210.03686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03686"}},"official":{"repos":["levan92/occlusion-copy-paste"],"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/effective-self-supervised-pre-training-on-low","slug":"effective-self-supervised-pre-training-on-low","title":"Effective Self-supervised Pre-training on Low-compute Networks without Distillation","date":"2022-10-06","arxiv_id":"2210.02808","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/effective-self-supervised-pre-training-on-low#ran","syntology_url":"https://syntology.ai/paper/2210.02808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02808"}},"official":{"repos":["saic-fi/sslight"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mask3d-for-3d-semantic-instance-segmentation","slug":"mask3d-for-3d-semantic-instance-segmentation","title":"Mask3D: Mask Transformer for 3D Semantic Instance Segmentation","date":"2022-10-06","arxiv_id":"2210.03105","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/mask3d-for-3d-semantic-instance-segmentation#ran","syntology_url":"https://syntology.ai/paper/2210.03105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03105"}},"official":{"repos":["jonasschult/mask3d"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"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/exploring-target-representations-for-masked","slug":"exploring-target-representations-for-masked","title":"Exploring Target Representations for Masked Autoencoders","date":"2022-09-08","arxiv_id":"2209.03917","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/exploring-target-representations-for-masked#ran","syntology_url":"https://syntology.ai/paper/2209.03917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.03917"}},"official":{"repos":["liuxingbin/dbot"],"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":["official"]}}},{"url":"/paper/refine-and-represent-region-to-object","slug":"refine-and-represent-region-to-object","title":"Refine and Represent: Region-to-Object Representation Learning","date":"2022-08-25","arxiv_id":"2208.11821","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/refine-and-represent-region-to-object#ran","syntology_url":"https://syntology.ai/paper/2208.11821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.11821"}},"official":{"repos":["kkallidromitis/r2o"],"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/unifying-visual-perception-by-dispersible","slug":"unifying-visual-perception-by-dispersible","title":"Unifying Visual Perception by Dispersible Points Learning","date":"2022-08-18","arxiv_id":"2208.08630","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/unifying-visual-perception-by-dispersible#ran","syntology_url":"https://syntology.ai/paper/2208.08630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08630"}},"official":{"repos":["sense-x/unihead"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"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/semantic-segmentation-assisted-instance","slug":"semantic-segmentation-assisted-instance","title":"Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation","date":"2022-08-09","arxiv_id":"2208.04766","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":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) · 1 unverified","sample_list":"/paper/semantic-segmentation-assisted-instance#ran","syntology_url":"https://syntology.ai/paper/2208.04766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.04766"}},"official":{"repos":["isunchy/3d_instance_segmentation"],"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/video-mask-transfiner-for-high-quality-video","slug":"video-mask-transfiner-for-high-quality-video","title":"Video Mask Transfiner for High-Quality Video Instance Segmentation","date":"2022-07-28","arxiv_id":"2207.14012","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/video-mask-transfiner-for-high-quality-video#ran","syntology_url":"https://syntology.ai/paper/2207.14012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14012"}},"official":null}},{"url":"/paper/visual-recognition-by-request","slug":"visual-recognition-by-request","title":"Visual Recognition by Request","date":"2022-07-28","arxiv_id":"2207.14227","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/visual-recognition-by-request#ran","syntology_url":"https://syntology.ai/paper/2207.14227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14227"}},"official":{"repos":["chufengt/ViRReq"],"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":["official"]}}},{"url":"/paper/compositional-human-scene-interaction","slug":"compositional-human-scene-interaction","title":"Compositional Human-Scene Interaction Synthesis with Semantic Control","date":"2022-07-26","arxiv_id":"2207.12824","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":5,"n_honours":1,"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; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/compositional-human-scene-interaction#ran","syntology_url":"https://syntology.ai/paper/2207.12824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.12824"}},"official":{"repos":["zkf1997/coins"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/geodesic-former-a-geodesic-guided-few-shot-3d","slug":"geodesic-former-a-geodesic-guided-few-shot-3d","title":"Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter","date":"2022-07-22","arxiv_id":"2207.10859","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/geodesic-former-a-geodesic-guided-few-shot-3d#ran","syntology_url":"https://syntology.ai/paper/2207.10859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10859"}},"official":{"repos":["vinairesearch/geoformer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/devis-making-deformable-transformers-work-for","slug":"devis-making-deformable-transformers-work-for","title":"DeVIS: Making Deformable Transformers Work for Video Instance Segmentation","date":"2022-07-22","arxiv_id":"2207.11103","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/devis-making-deformable-transformers-work-for#ran","syntology_url":"https://syntology.ai/paper/2207.11103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.11103"}},"official":{"repos":["acaelles97/devis"],"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/divide-and-conquer-3d-point-cloud-instance","slug":"divide-and-conquer-3d-point-cloud-instance","title":"Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization","date":"2022-07-22","arxiv_id":"2207.11209","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"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/divide-and-conquer-3d-point-cloud-instance#ran","syntology_url":"https://syntology.ai/paper/2207.11209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.11209"}},"official":{"repos":["weiguangzhao/PBNet"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/in-defense-of-online-models-for-video","slug":"in-defense-of-online-models-for-video","title":"In Defense of Online Models for Video Instance Segmentation","date":"2022-07-21","arxiv_id":"2207.10661","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/in-defense-of-online-models-for-video#ran","syntology_url":"https://syntology.ai/paper/2207.10661","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10661"}},"official":{"repos":["wjf5203/vnext"],"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/3d-instances-as-1d-kernels","slug":"3d-instances-as-1d-kernels","title":"3D Instances as 1D Kernels","date":"2022-07-15","arxiv_id":"2207.07372","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/3d-instances-as-1d-kernels#ran","syntology_url":"https://syntology.ai/paper/2207.07372","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07372"}},"official":{"repos":["w1zheng/dknet"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/wave-vit-unifying-wavelet-and-transformers","slug":"wave-vit-unifying-wavelet-and-transformers","title":"Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning","date":"2022-07-11","arxiv_id":"2207.04978","repositories_listed":3,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/wave-vit-unifying-wavelet-and-transformers#ran","syntology_url":"https://syntology.ai/paper/2207.04978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04978"}},"official":{"repos":["yehli/imagenetmodel"],"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/vision-based-uneven-bev-representation","slug":"vision-based-uneven-bev-representation","title":"Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation","date":"2022-07-05","arxiv_id":"2207.01878","repositories_listed":1,"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":1,"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/vision-based-uneven-bev-representation#ran","syntology_url":"https://syntology.ai/paper/2207.01878","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01878"}},"official":{"repos":["superz-liu/polarbev"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/segmenting-moving-objects-via-an-object","slug":"segmenting-moving-objects-via-an-object","title":"Segmenting Moving Objects via an Object-Centric Layered Representation","date":"2022-07-05","arxiv_id":"2207.02206","repositories_listed":1,"syntology":{"n":21,"n_ran":16,"n_constructed":9,"n_ran_checked":12,"n_instrument":4,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"16 ran (of which 9 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/segmenting-moving-objects-via-an-object#ran","syntology_url":"https://syntology.ai/paper/2207.02206","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02206"}},"official":{"repos":["Jyxarthur/OCLR_model"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":9,"n_ran_no_instrument_failure":12,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/revbifpn-the-fully-reversible-bidirectional","slug":"revbifpn-the-fully-reversible-bidirectional","title":"RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network","date":"2022-06-28","arxiv_id":"2206.14098","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/revbifpn-the-fully-reversible-bidirectional#ran","syntology_url":"https://syntology.ai/paper/2206.14098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.14098"}},"official":{"repos":["cerebrasresearch/revbifpn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/global-context-vision-transformers","slug":"global-context-vision-transformers","title":"Global Context Vision Transformers","date":"2022-06-20","arxiv_id":"2206.09959","repositories_listed":8,"syntology":{"n":36,"n_ran":21,"n_constructed":8,"n_ran_checked":17,"n_instrument":4,"n_unverified":15,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":15,"phrase":"21 ran (of which 8 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 4 where Syntology's instrument failed) · 15 unverified","sample_list":"/paper/global-context-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2206.09959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09959"}},"official":{"repos":["nvlabs/gcvit"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":8,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/patch-level-representation-learning-for-self-1","slug":"patch-level-representation-learning-for-self-1","title":"Patch-level Representation Learning for Self-supervised Vision Transformers","date":"2022-06-16","arxiv_id":"2206.07990","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/patch-level-representation-learning-for-self-1#ran","syntology_url":"https://syntology.ai/paper/2206.07990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07990"}},"official":{"repos":["alinlab/selfpatch"],"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/rf-next-efficient-receptive-field-search-for","slug":"rf-next-efficient-receptive-field-search-for","title":"RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks","date":"2022-06-14","arxiv_id":"2206.06637","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rf-next-efficient-receptive-field-search-for#ran","syntology_url":"https://syntology.ai/paper/2206.06637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06637"}},"official":{"repos":["ShangHua-Gao/RFNext"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/glipv2-unifying-localization-and-vision","slug":"glipv2-unifying-localization-and-vision","title":"GLIPv2: Unifying Localization and Vision-Language Understanding","date":"2022-06-12","arxiv_id":"2206.05836","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/glipv2-unifying-localization-and-vision#ran","syntology_url":"https://syntology.ai/paper/2206.05836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.05836"}},"official":{"repos":["microsoft/GLIP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/vita-video-instance-segmentation-via-object","slug":"vita-video-instance-segmentation-via-object","title":"VITA: Video Instance Segmentation via Object Token Association","date":"2022-06-09","arxiv_id":"2206.04403","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/vita-video-instance-segmentation-via-object#ran","syntology_url":"https://syntology.ai/paper/2206.04403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.04403"}},"official":{"repos":["sukjunhwang/vita"],"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/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/metrics-reloaded-pitfalls-and-recommendations","slug":"metrics-reloaded-pitfalls-and-recommendations","title":"Metrics reloaded: Recommendations for image analysis validation","date":"2022-06-03","arxiv_id":"2206.01653","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/metrics-reloaded-pitfalls-and-recommendations#ran","syntology_url":"https://syntology.ai/paper/2206.01653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.01653"}},"official":{"repos":["project-monai/metricsreloaded"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-visual-representation-2","slug":"self-supervised-visual-representation-2","title":"Self-Supervised Visual Representation Learning with Semantic Grouping","date":"2022-05-30","arxiv_id":"2205.15288","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"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 2 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/self-supervised-visual-representation-2#ran","syntology_url":"https://syntology.ai/paper/2205.15288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15288"}},"official":{"repos":["CVMI-Lab/SlotCon"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/efficientvit-enhanced-linear-attention-for","slug":"efficientvit-enhanced-linear-attention-for","title":"EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction","date":"2022-05-29","arxiv_id":"2205.14756","repositories_listed":6,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficientvit-enhanced-linear-attention-for#ran","syntology_url":"https://syntology.ai/paper/2205.14756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14756"}},"official":{"repos":["mit-han-lab/efficientvit","rwightman/pytorch-image-models"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-learning-rivals-masked-image","slug":"contrastive-learning-rivals-masked-image","title":"Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation","date":"2022-05-27","arxiv_id":"2205.14141","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"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) · 5 unverified","sample_list":"/paper/contrastive-learning-rivals-masked-image#ran","syntology_url":"https://syntology.ai/paper/2205.14141","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14141"}},"official":{"repos":["SwinTransformer/Feature-Distillation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/human-instance-matting-via-mutual-guidance","slug":"human-instance-matting-via-mutual-guidance","title":"Human Instance Matting via Mutual Guidance and Multi-Instance Refinement","date":"2022-05-22","arxiv_id":"2205.10767","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/human-instance-matting-via-mutual-guidance#ran","syntology_url":"https://syntology.ai/paper/2205.10767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10767"}},"official":{"repos":["nowsyn/instmatt"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/plane-geometry-diagram-parsing","slug":"plane-geometry-diagram-parsing","title":"Plane Geometry Diagram Parsing","date":"2022-05-19","arxiv_id":"2205.09363","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/plane-geometry-diagram-parsing#ran","syntology_url":"https://syntology.ai/paper/2205.09363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.09363"}},"official":{"repos":["mingliangzhang2018/PGDP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/grit-general-robust-image-task-benchmark","slug":"grit-general-robust-image-task-benchmark","title":"GRIT: General Robust Image Task Benchmark","date":"2022-04-28","arxiv_id":"2204.13653","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/grit-general-robust-image-task-benchmark#ran","syntology_url":"https://syntology.ai/paper/2204.13653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.13653"}},"official":{"repos":["allenai/grit_official"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/polyloss-a-polynomial-expansion-perspective-1","slug":"polyloss-a-polynomial-expansion-perspective-1","title":"PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions","date":"2022-04-26","arxiv_id":"2204.12511","repositories_listed":19,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/polyloss-a-polynomial-expansion-perspective-1#ran","syntology_url":"https://syntology.ai/paper/2204.12511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12511"}},"official":null}},{"url":"/paper/temporally-efficient-vision-transformer-for","slug":"temporally-efficient-vision-transformer-for","title":"Temporally Efficient Vision Transformer for Video Instance Segmentation","date":"2022-04-18","arxiv_id":"2204.08412","repositories_listed":3,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/temporally-efficient-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2204.08412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08412"}},"official":{"repos":["hustvl/tevit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/vsa-learning-varied-size-window-attention-in","slug":"vsa-learning-varied-size-window-attention-in","title":"VSA: Learning Varied-Size Window Attention in Vision Transformers","date":"2022-04-18","arxiv_id":"2204.08446","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/vsa-learning-varied-size-window-attention-in#ran","syntology_url":"https://syntology.ai/paper/2204.08446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08446"}},"official":{"repos":["vitae-transformer/vitae-vsa"],"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":["listed","official"]}}},{"url":"/paper/an-extendable-efficient-and-effective","slug":"an-extendable-efficient-and-effective","title":"An Extendable, Efficient and Effective Transformer-based Object Detector","date":"2022-04-17","arxiv_id":"2204.07962","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":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) · 1 unverified","sample_list":"/paper/an-extendable-efficient-and-effective#ran","syntology_url":"https://syntology.ai/paper/2204.07962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07962"}},"official":{"repos":["naver-ai/vidt"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["community","official"]}}},{"url":"/paper/open-world-instance-segmentation-exploiting","slug":"open-world-instance-segmentation-exploiting","title":"Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity","date":"2022-04-12","arxiv_id":"2204.06107","repositories_listed":1,"syntology":{"n":20,"n_ran":11,"n_constructed":0,"n_ran_checked":2,"n_instrument":9,"n_unverified":9,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":20,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 9 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/open-world-instance-segmentation-exploiting#ran","syntology_url":"https://syntology.ai/paper/2204.06107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.06107"}},"official":{"repos":["facebookresearch/Generic-Grouping"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/davit-dual-attention-vision-transformers","slug":"davit-dual-attention-vision-transformers","title":"DaViT: Dual Attention Vision Transformers","date":"2022-04-07","arxiv_id":"2204.03645","repositories_listed":4,"syntology":{"n":15,"n_ran":8,"n_constructed":5,"n_ran_checked":6,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 5 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) · 7 unverified","sample_list":"/paper/davit-dual-attention-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2204.03645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03645"}},"official":{"repos":["dingmyu/davit"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-plain-vision-transformer-backbones","slug":"exploring-plain-vision-transformer-backbones","title":"Exploring Plain Vision Transformer Backbones for Object Detection","date":"2022-03-30","arxiv_id":"2203.16527","repositories_listed":11,"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":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploring-plain-vision-transformer-backbones#ran","syntology_url":"https://syntology.ai/paper/2203.16527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16527"}},"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/mc-beit-multi-choice-discretization-for-image","slug":"mc-beit-multi-choice-discretization-for-image","title":"mc-BEiT: Multi-choice Discretization for Image BERT Pre-training","date":"2022-03-29","arxiv_id":"2203.15371","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mc-beit-multi-choice-discretization-for-image#ran","syntology_url":"https://syntology.ai/paper/2203.15371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15371"}},"official":{"repos":["lixiaotong97/mc-beit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sepvit-separable-vision-transformer","slug":"sepvit-separable-vision-transformer","title":"SepViT: Separable Vision Transformer","date":"2022-03-29","arxiv_id":"2203.15380","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sepvit-separable-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2203.15380","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15380"}},"official":{"repos":["liwei109/sepvit"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/chex-channel-exploration-for-cnn-model","slug":"chex-channel-exploration-for-cnn-model","title":"CHEX: CHannel EXploration for CNN Model Compression","date":"2022-03-29","arxiv_id":"2203.15794","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chex-channel-exploration-for-cnn-model#ran","syntology_url":"https://syntology.ai/paper/2203.15794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15794"}},"official":null}},{"url":"/paper/sparse-instance-activation-for-real-time","slug":"sparse-instance-activation-for-real-time","title":"Sparse Instance Activation for Real-Time Instance Segmentation","date":"2022-03-24","arxiv_id":"2203.12827","repositories_listed":2,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sparse-instance-activation-for-real-time#ran","syntology_url":"https://syntology.ai/paper/2203.12827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12827"}},"official":{"repos":["hustvl/sparseinst"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-patch-to-cluster-attention-in-vision","slug":"learning-patch-to-cluster-attention-in-vision","title":"PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers","date":"2022-03-22","arxiv_id":"2203.11987","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-patch-to-cluster-attention-in-vision#ran","syntology_url":"https://syntology.ai/paper/2203.11987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11987"}},"official":{"repos":["ivmcl/pacavit"],"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/generating-fast-and-slow-scene-decomposition","slug":"generating-fast-and-slow-scene-decomposition","title":"Test-time Adaptation with Slot-Centric Models","date":"2022-03-21","arxiv_id":"2203.11194","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generating-fast-and-slow-scene-decomposition#ran","syntology_url":"https://syntology.ai/paper/2203.11194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11194"}},"official":{"repos":["mihirp1998/Slot-TTA"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/e2ec-an-end-to-end-contour-based-method-for","slug":"e2ec-an-end-to-end-contour-based-method-for","title":"E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation","date":"2022-03-08","arxiv_id":"2203.04074","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":4,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":10,"phrase":"8 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/e2ec-an-end-to-end-contour-based-method-for#ran","syntology_url":"https://syntology.ai/paper/2203.04074","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04074"}},"official":{"repos":["zhang-tao-whu/e2ec"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":2,"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/nuclei-segmentation-and-classification-in","slug":"nuclei-segmentation-and-classification-in","title":"Nuclei instance segmentation and classification in histopathology images with StarDist","date":"2022-03-03","arxiv_id":"2203.02284","repositories_listed":3,"syntology":{"n":15,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/nuclei-segmentation-and-classification-in#ran","syntology_url":"https://syntology.ai/paper/2203.02284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02284"}},"official":{"repos":["stardist/stardist"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","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/relieving-long-tailed-instance-segmentation","slug":"relieving-long-tailed-instance-segmentation","title":"Relieving Long-tailed Instance Segmentation via Pairwise Class Balance","date":"2022-01-08","arxiv_id":"2201.02784","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"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 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/relieving-long-tailed-instance-segmentation#ran","syntology_url":"https://syntology.ai/paper/2201.02784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.02784"}},"official":{"repos":["megvii-research/pcb"],"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/pale-transformer-a-general-vision-transformer","slug":"pale-transformer-a-general-vision-transformer","title":"Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention","date":"2021-12-28","arxiv_id":"2112.14000","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pale-transformer-a-general-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2112.14000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.14000"}},"official":{"repos":["BR-IDL/PaddleViT"],"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/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/elsa-enhanced-local-self-attention-for-vision","slug":"elsa-enhanced-local-self-attention-for-vision","title":"ELSA: Enhanced Local Self-Attention for Vision Transformer","date":"2021-12-23","arxiv_id":"2112.12786","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/elsa-enhanced-local-self-attention-for-vision#ran","syntology_url":"https://syntology.ai/paper/2112.12786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.12786"}},"official":{"repos":["damo-cv/elsa"],"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/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/visolo-grid-based-space-time-aggregation-for","slug":"visolo-grid-based-space-time-aggregation-for","title":"VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation","date":"2021-12-08","arxiv_id":"2112.04177","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/visolo-grid-based-space-time-aggregation-for#ran","syntology_url":"https://syntology.ai/paper/2112.04177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.04177"}},"official":{"repos":["suhohan95/visolo"],"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/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/polyworld-polygonal-building-extraction-with","slug":"polyworld-polygonal-building-extraction-with","title":"PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images","date":"2021-11-30","arxiv_id":"2111.15491","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/polyworld-polygonal-building-extraction-with#ran","syntology_url":"https://syntology.ai/paper/2111.15491","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.15491"}},"official":{"repos":["zorzi-s/polyworldpretrainednetwork"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-referring-video-object","slug":"end-to-end-referring-video-object","title":"End-to-End Referring Video Object Segmentation with Multimodal Transformers","date":"2021-11-29","arxiv_id":"2111.14821","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"9 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; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/end-to-end-referring-video-object#ran","syntology_url":"https://syntology.ai/paper/2111.14821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.14821"}},"official":{"repos":["mttr2021/MTTR"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mask-transfiner-for-high-quality-instance","slug":"mask-transfiner-for-high-quality-instance","title":"Mask Transfiner for High-Quality Instance Segmentation","date":"2021-11-26","arxiv_id":"2111.13673","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mask-transfiner-for-high-quality-instance#ran","syntology_url":"https://syntology.ai/paper/2111.13673","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13673"}},"official":{"repos":["SysCV/transfiner"],"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/conditional-object-centric-learning-from-1","slug":"conditional-object-centric-learning-from-1","title":"Conditional Object-Centric Learning from Video","date":"2021-11-24","arxiv_id":"2111.12594","repositories_listed":3,"syntology":{"n":13,"n_ran":11,"n_constructed":8,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":10,"phrase":"11 ran (of which 8 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) · 2 unverified","sample_list":"/paper/conditional-object-centric-learning-from-1#ran","syntology_url":"https://syntology.ai/paper/2111.12594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.12594"}},"official":{"repos":["google-research/slot-attention-video"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/open-vocabulary-instance-segmentation-via","slug":"open-vocabulary-instance-segmentation-via","title":"Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling","date":"2021-11-24","arxiv_id":"2111.12698","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/open-vocabulary-instance-segmentation-via#ran","syntology_url":"https://syntology.ai/paper/2111.12698","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.12698"}},"official":{"repos":["hbdat/cvpr22_cross_modal_pseudo_labeling"],"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/swin-transformer-v2-scaling-up-capacity-and","slug":"swin-transformer-v2-scaling-up-capacity-and","title":"Swin Transformer V2: Scaling Up Capacity and Resolution","date":"2021-11-18","arxiv_id":"2111.09883","repositories_listed":23,"syntology":{"n":30,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":13,"n_honours":0,"n_violates":3,"n_no_contract":14,"n_pointer_only":4,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 3 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/swin-transformer-v2-scaling-up-capacity-and#ran","syntology_url":"https://syntology.ai/paper/2111.09883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09883"}},"official":{"repos":["microsoft/Swin-Transformer"],"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/recurrence-along-depth-deep-convolutional","slug":"recurrence-along-depth-deep-convolutional","title":"Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation","date":"2021-10-22","arxiv_id":"2110.11852","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"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 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/recurrence-along-depth-deep-convolutional#ran","syntology_url":"https://syntology.ai/paper/2110.11852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11852"}},"official":{"repos":["fangyanwen1106/RLANet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/planerecnet-multi-task-learning-with-cross","slug":"planerecnet-multi-task-learning-with-cross","title":"PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image","date":"2021-10-21","arxiv_id":"2110.11219","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/planerecnet-multi-task-learning-with-cross#ran","syntology_url":"https://syntology.ai/paper/2110.11219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11219"}},"official":{"repos":["eryixie/planerecnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learn-then-test-calibrating-predictive","slug":"learn-then-test-calibrating-predictive","title":"Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control","date":"2021-10-03","arxiv_id":"2110.01052","repositories_listed":2,"syntology":{"n":22,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":5,"n_honours":3,"n_violates":3,"n_no_contract":11,"n_pointer_only":3,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 3 honoured, 3 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/learn-then-test-calibrating-predictive#ran","syntology_url":"https://syntology.ai/paper/2110.01052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01052"}},"official":{"repos":["aangelopoulos/ltt"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/instance-segmentation-in-3d-scenes-using","slug":"instance-segmentation-in-3d-scenes-using","title":"Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks","date":"2021-08-17","arxiv_id":"2108.07478","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/instance-segmentation-in-3d-scenes-using#ran","syntology_url":"https://syntology.ai/paper/2108.07478","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07478"}},"official":{"repos":["gorilla-lab-scut/sstnet"],"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/exploring-classification-equilibrium-in-long","slug":"exploring-classification-equilibrium-in-long","title":"Exploring Classification Equilibrium in Long-Tailed Object Detection","date":"2021-08-17","arxiv_id":"2108.07507","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"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/exploring-classification-equilibrium-in-long#ran","syntology_url":"https://syntology.ai/paper/2108.07507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07507"}},"official":{"repos":["fcjian/loce"],"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/sotr-segmenting-objects-with-transformers","slug":"sotr-segmenting-objects-with-transformers","title":"SOTR: Segmenting Objects with Transformers","date":"2021-08-15","arxiv_id":"2108.06747","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":4,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 2 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sotr-segmenting-objects-with-transformers#ran","syntology_url":"https://syntology.ai/paper/2108.06747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06747"}},"official":{"repos":["easton-cau/SOTR"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/uninet-a-unified-scene-understanding-network","slug":"uninet-a-unified-scene-understanding-network","title":"UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks","date":"2021-08-10","arxiv_id":"2108.04584","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/uninet-a-unified-scene-understanding-network#ran","syntology_url":"https://syntology.ai/paper/2108.04584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04584"}},"official":{"repos":["NeurAI-Lab/UniNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/crossformer-a-versatile-vision-transformer","slug":"crossformer-a-versatile-vision-transformer","title":"CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention","date":"2021-07-31","arxiv_id":"2108.00154","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/crossformer-a-versatile-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2108.00154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00154"}},"official":{"repos":["cheerss/CrossFormer"],"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/cyclemlp-a-mlp-like-architecture-for-dense","slug":"cyclemlp-a-mlp-like-architecture-for-dense","title":"CycleMLP: A MLP-like Architecture for Dense Prediction","date":"2021-07-21","arxiv_id":"2107.10224","repositories_listed":8,"syntology":{"n":15,"n_ran":10,"n_constructed":6,"n_ran_checked":10,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":2,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/cyclemlp-a-mlp-like-architecture-for-dense#ran","syntology_url":"https://syntology.ai/paper/2107.10224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.10224"}},"official":{"repos":["ShoufaChen/CycleMLP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["listed","official","unlocated"]}}}],"record_sha256":"93f82c302d839729866e054a26e3274940383ad4702d6436d872dfbd10b9e51c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}