{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/object-recognition/papers/ran/1","list_of":"/task/object-recognition","task":"Object Recognition","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":2,"rows_per_page":100,"rows":[1,100],"of":132,"counts":{"archive_papers_tagged":2042,"with_a_code_link":577,"where_syntology_ran_a_sample":132,"not_listed_spam_title":0,"listed":2042,"listed_where_code_ran":132,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":100,"every_run_a_failure_of_syntologys_instrument":32,"listed_with_a_run_with_no_instrument_failure":100,"listed_every_run_a_failure_of_syntologys_instrument":32,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/object-recognition/papers/ran/1","prev":null,"next":"/task/object-recognition/papers/ran/2","papers":[{"url":"/paper/sasep-saliency-aware-structured-separation-of-1","slug":"sasep-saliency-aware-structured-separation-of-1","title":"SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds","date":"2025-06-16","arxiv_id":"2506.13224","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sasep-saliency-aware-structured-separation-of-1#ran","syntology_url":"https://syntology.ai/paper/2506.13224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.13224"}},"official":{"repos":["jinfengx/sasep"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/quantum-doubly-stochastic-transformers","slug":"quantum-doubly-stochastic-transformers","title":"Quantum Doubly Stochastic Transformers","date":"2025-04-22","arxiv_id":"2504.16275","repositories_listed":0,"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":1,"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/quantum-doubly-stochastic-transformers#ran","syntology_url":"https://syntology.ai/paper/2504.16275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.16275"}},"official":null}},{"url":"/paper/wisead-knowledge-augmented-end-to-end","slug":"wisead-knowledge-augmented-end-to-end","title":"WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model","date":"2024-12-13","arxiv_id":"2412.09951","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/wisead-knowledge-augmented-end-to-end#ran","syntology_url":"https://syntology.ai/paper/2412.09951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.09951"}},"official":{"repos":["wyddmw/WiseAD"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lvlm-count-enhancing-the-counting-ability-of","slug":"lvlm-count-enhancing-the-counting-ability-of","title":"LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models","date":"2024-12-01","arxiv_id":"2412.00686","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/lvlm-count-enhancing-the-counting-ability-of#ran","syntology_url":"https://syntology.ai/paper/2412.00686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.00686"}},"official":{"repos":["mrghofrani/lvlm-count"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-where-to-edit-vision-transformers","slug":"learning-where-to-edit-vision-transformers","title":"Learning Where to Edit Vision Transformers","date":"2024-11-04","arxiv_id":"2411.01948","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-where-to-edit-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2411.01948","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.01948"}},"official":{"repos":["hustyyq/where-to-edit"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/momentumsmoe-integrating-momentum-into-sparse","slug":"momentumsmoe-integrating-momentum-into-sparse","title":"MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts","date":"2024-10-18","arxiv_id":"2410.14574","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 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) · 2 unverified","sample_list":"/paper/momentumsmoe-integrating-momentum-into-sparse#ran","syntology_url":"https://syntology.ai/paper/2410.14574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14574"}},"official":{"repos":["rachtsy/momentumsmoe"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dawin-training-free-dynamic-weight","slug":"dawin-training-free-dynamic-weight","title":"DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation","date":"2024-10-03","arxiv_id":"2410.03782","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":2,"n_ran_checked":3,"n_instrument":5,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"8 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/dawin-training-free-dynamic-weight#ran","syntology_url":"https://syntology.ai/paper/2410.03782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.03782"}},"official":{"repos":["naver-ai/dawin"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/unibench-visual-reasoning-requires-rethinking","slug":"unibench-visual-reasoning-requires-rethinking","title":"UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling","date":"2024-08-09","arxiv_id":"2408.04810","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":9,"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/unibench-visual-reasoning-requires-rethinking#ran","syntology_url":"https://syntology.ai/paper/2408.04810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.04810"}},"official":{"repos":["facebookresearch/unibench"],"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/marvelovd-marrying-object-recognition-and","slug":"marvelovd-marrying-object-recognition-and","title":"MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object Detection","date":"2024-07-31","arxiv_id":"2407.21465","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/marvelovd-marrying-object-recognition-and#ran","syntology_url":"https://syntology.ai/paper/2407.21465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.21465"}},"official":{"repos":["wkfdb/marvelovd"],"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/partimagenet-dataset-scaling-up-part-based","slug":"partimagenet-dataset-scaling-up-part-based","title":"PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition","date":"2024-07-15","arxiv_id":"2407.10918","repositories_listed":1,"syntology":{"n":19,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/partimagenet-dataset-scaling-up-part-based#ran","syntology_url":"https://syntology.ai/paper/2407.10918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10918"}},"official":{"repos":["LixiaoTHU/PartImageNetPP"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/comics-datasets-framework-mix-of-comics","slug":"comics-datasets-framework-mix-of-comics","title":"Comics Datasets Framework: Mix of Comics datasets for detection benchmarking","date":"2024-07-03","arxiv_id":"2407.03540","repositories_listed":2,"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/comics-datasets-framework-mix-of-comics#ran","syntology_url":"https://syntology.ai/paper/2407.03540","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.03540"}},"official":{"repos":["emanuelevivoli/cdf"],"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/efficient-event-stream-super-resolution-with","slug":"efficient-event-stream-super-resolution-with","title":"Efficient Event Stream Super-Resolution with Recursive Multi-Branch Fusion","date":"2024-06-28","arxiv_id":"2406.19640","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"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 0 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/efficient-event-stream-super-resolution-with#ran","syntology_url":"https://syntology.ai/paper/2406.19640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.19640"}},"official":{"repos":["lqm26/rmfnet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mg-llava-towards-multi-granularity-visual","slug":"mg-llava-towards-multi-granularity-visual","title":"MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning","date":"2024-06-25","arxiv_id":"2406.17770","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/mg-llava-towards-multi-granularity-visual#ran","syntology_url":"https://syntology.ai/paper/2406.17770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17770"}},"official":{"repos":["phoenixz810/mg-llava"],"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/the-3d-pc-a-benchmark-for-visual-perspective","slug":"the-3d-pc-a-benchmark-for-visual-perspective","title":"The 3D-PC: a benchmark for visual perspective taking in humans and machines","date":"2024-06-06","arxiv_id":"2406.04138","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-3d-pc-a-benchmark-for-visual-perspective#ran","syntology_url":"https://syntology.ai/paper/2406.04138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04138"}},"official":{"repos":["serre-lab/VPT"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bilateral-event-mining-and-complementary-for","slug":"bilateral-event-mining-and-complementary-for","title":"Bilateral Event Mining and Complementary for Event Stream Super-Resolution","date":"2024-05-16","arxiv_id":"2405.10037","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bilateral-event-mining-and-complementary-for#ran","syntology_url":"https://syntology.ai/paper/2405.10037","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.10037"}},"official":{"repos":["lqm26/bmcnet-esr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/two-effects-one-trigger-on-the-modality-gap","slug":"two-effects-one-trigger-on-the-modality-gap","title":"Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models","date":"2024-04-11","arxiv_id":"2404.07983","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/two-effects-one-trigger-on-the-modality-gap#ran","syntology_url":"https://syntology.ai/paper/2404.07983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07983"}},"official":{"repos":["lmb-freiburg/two-effects-one-trigger"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mindset-vision-a-toolbox-for-testing-dnns-on","slug":"mindset-vision-a-toolbox-for-testing-dnns-on","title":"MindSet: Vision. A toolbox for testing DNNs on key psychological experiments","date":"2024-04-08","arxiv_id":"2404.05290","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":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/mindset-vision-a-toolbox-for-testing-dnns-on#ran","syntology_url":"https://syntology.ai/paper/2404.05290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05290"}},"official":{"repos":["mindsetvision/mindset-vision"],"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/is-clip-the-main-roadblock-for-fine-grained","slug":"is-clip-the-main-roadblock-for-fine-grained","title":"Is CLIP the main roadblock for fine-grained open-world perception?","date":"2024-04-04","arxiv_id":"2404.03539","repositories_listed":2,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":12,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":17,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/is-clip-the-main-roadblock-for-fine-grained#ran","syntology_url":"https://syntology.ai/paper/2404.03539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03539"}},"official":{"repos":["lorebianchi98/fg-clip"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/eventrpg-event-data-augmentation-with","slug":"eventrpg-event-data-augmentation-with","title":"EventRPG: Event Data Augmentation with Relevance Propagation Guidance","date":"2024-03-14","arxiv_id":"2403.09274","repositories_listed":1,"syntology":{"n":16,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":11,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/eventrpg-event-data-augmentation-with#ran","syntology_url":"https://syntology.ai/paper/2403.09274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09274"}},"official":{"repos":["myuansun/eventrpg"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/mikasa-multi-key-anchor-scene-aware","slug":"mikasa-multi-key-anchor-scene-aware","title":"MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding","date":"2024-03-05","arxiv_id":"2403.03077","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":10,"n_ran_checked":11,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":16,"phrase":"11 ran (of which 10 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) · 5 unverified","sample_list":"/paper/mikasa-multi-key-anchor-scene-aware#ran","syntology_url":"https://syntology.ai/paper/2403.03077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03077"}},"official":{"repos":["dfki-av/mikasa-3dvg"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":10,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/dual-pose-invariant-embeddings-learning","slug":"dual-pose-invariant-embeddings-learning","title":"Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval","date":"2024-03-01","arxiv_id":"2403.00272","repositories_listed":0,"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":2,"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/dual-pose-invariant-embeddings-learning#ran","syntology_url":"https://syntology.ai/paper/2403.00272","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00272"}},"official":null}},{"url":"/paper/shield-an-evaluation-benchmark-for-face","slug":"shield-an-evaluation-benchmark-for-face","title":"SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models","date":"2024-02-06","arxiv_id":"2402.04178","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/shield-an-evaluation-benchmark-for-face#ran","syntology_url":"https://syntology.ai/paper/2402.04178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04178"}},"official":{"repos":["laiyingxin2/shield"],"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/pix2gestalt-amodal-segmentation-by","slug":"pix2gestalt-amodal-segmentation-by","title":"pix2gestalt: Amodal Segmentation by Synthesizing Wholes","date":"2024-01-25","arxiv_id":"2401.14398","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":3,"n_no_contract":2,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 3 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pix2gestalt-amodal-segmentation-by#ran","syntology_url":"https://syntology.ai/paper/2401.14398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14398"}},"official":{"repos":["cvlab-columbia/pix2gestalt"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/are-vision-transformers-more-data-hungry-than-1","slug":"are-vision-transformers-more-data-hungry-than-1","title":"Are Vision Transformers More Data Hungry Than Newborn Visual Systems?","date":"2023-12-05","arxiv_id":"2312.02843","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/are-vision-transformers-more-data-hungry-than-1#ran","syntology_url":"https://syntology.ai/paper/2312.02843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02843"}},"official":{"repos":["buildingamind/vit-cot"],"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/e2pnet-event-to-point-cloud-registration-with-1","slug":"e2pnet-event-to-point-cloud-registration-with-1","title":"E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning","date":"2023-11-30","arxiv_id":"2311.18433","repositories_listed":1,"syntology":{"n":9,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":9,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/e2pnet-event-to-point-cloud-registration-with-1#ran","syntology_url":"https://syntology.ai/paper/2311.18433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.18433"}},"official":{"repos":["xmu-qcj/e2pnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/recognize-any-regions","slug":"recognize-any-regions","title":"Recognize Any Regions","date":"2023-11-02","arxiv_id":"2311.01373","repositories_listed":1,"syntology":{"n":11,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":11,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/recognize-any-regions#ran","syntology_url":"https://syntology.ai/paper/2311.01373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01373"}},"official":{"repos":["surrey-uplab/recognize-any-regions"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/intriguing-properties-of-generative","slug":"intriguing-properties-of-generative","title":"Intriguing properties of generative classifiers","date":"2023-09-28","arxiv_id":"2309.16779","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/intriguing-properties-of-generative#ran","syntology_url":"https://syntology.ai/paper/2309.16779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16779"}},"official":null}},{"url":"/paper/lmc-large-model-collaboration-with-cross-1","slug":"lmc-large-model-collaboration-with-cross-1","title":"LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition","date":"2023-09-22","arxiv_id":"2309.12780","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lmc-large-model-collaboration-with-cross-1#ran","syntology_url":"https://syntology.ai/paper/2309.12780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.12780"}},"official":{"repos":["harryqu123/lmc"],"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/decoding-natural-images-from-eeg-for-object","slug":"decoding-natural-images-from-eeg-for-object","title":"Decoding Natural Images from EEG for Object Recognition","date":"2023-08-25","arxiv_id":"2308.13234","repositories_listed":4,"syntology":{"n":12,"n_ran":9,"n_constructed":5,"n_ran_checked":8,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 ran (of which 5 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/decoding-natural-images-from-eeg-for-object#ran","syntology_url":"https://syntology.ai/paper/2308.13234","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13234"}},"official":{"repos":["eeyhsong/nice-eeg"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/label-free-event-based-object-recognition-via","slug":"label-free-event-based-object-recognition-via","title":"Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events","date":"2023-08-18","arxiv_id":"2308.09383","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/label-free-event-based-object-recognition-via#ran","syntology_url":"https://syntology.ai/paper/2308.09383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09383"}},"official":{"repos":["chohoonhee/ev-lafor"],"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/towards-language-models-that-can-see-computer","slug":"towards-language-models-that-can-see-computer","title":"Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language","date":"2023-06-28","arxiv_id":"2306.16410","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-language-models-that-can-see-computer#ran","syntology_url":"https://syntology.ai/paper/2306.16410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16410"}},"official":{"repos":["contextualai/lens"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/desco-learning-object-recognition-with-rich","slug":"desco-learning-object-recognition-with-rich","title":"DesCo: Learning Object Recognition with Rich Language Descriptions","date":"2023-06-24","arxiv_id":"2306.14060","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/desco-learning-object-recognition-with-rich#ran","syntology_url":"https://syntology.ai/paper/2306.14060","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14060"}},"official":null}},{"url":"/paper/eventclip-adapting-clip-for-event-based","slug":"eventclip-adapting-clip-for-event-based","title":"EventCLIP: Adapting CLIP for Event-based Object Recognition","date":"2023-06-10","arxiv_id":"2306.06354","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/eventclip-adapting-clip-for-event-based#ran","syntology_url":"https://syntology.ai/paper/2306.06354","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06354"}},"official":{"repos":["Wuziyi616/EventCLIP"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/paxion-patching-action-knowledge-in-video-1","slug":"paxion-patching-action-knowledge-in-video-1","title":"Paxion: Patching Action Knowledge in Video-Language Foundation Models","date":"2023-05-18","arxiv_id":"2305.10683","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/paxion-patching-action-knowledge-in-video-1#ran","syntology_url":"https://syntology.ai/paper/2305.10683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10683"}},"official":{"repos":["mikewangwzhl/paxion"],"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/learning-semi-supervised-gaussian-mixture","slug":"learning-semi-supervised-gaussian-mixture","title":"Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery","date":"2023-05-10","arxiv_id":"2305.06144","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-semi-supervised-gaussian-mixture#ran","syntology_url":"https://syntology.ai/paper/2305.06144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06144"}},"official":{"repos":["DTennant/GPC"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/discover-and-cure-concept-aware-mitigation-of","slug":"discover-and-cure-concept-aware-mitigation-of","title":"Discover and Cure: Concept-aware Mitigation of Spurious Correlation","date":"2023-05-01","arxiv_id":"2305.00650","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":1,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/discover-and-cure-concept-aware-mitigation-of#ran","syntology_url":"https://syntology.ai/paper/2305.00650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00650"}},"official":{"repos":["wuyxin/disc"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-generalization-in-robust-invariant","slug":"domain-generalization-in-robust-invariant","title":"Domain Generalization In Robust Invariant Representation","date":"2023-04-07","arxiv_id":"2304.03431","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/domain-generalization-in-robust-invariant#ran","syntology_url":"https://syntology.ai/paper/2304.03431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03431"}},"official":{"repos":["GauriGupta19/Domain-Generalisation-in-Invariance"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-efficient-coding-of-natural-images","slug":"learning-efficient-coding-of-natural-images","title":"Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations","date":"2023-03-06","arxiv_id":"2303.03307","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-efficient-coding-of-natural-images#ran","syntology_url":"https://syntology.ai/paper/2303.03307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.03307"}},"official":{"repos":["ThomasYerxa/mmcr"],"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/domain-aware-triplet-loss-in-domain","slug":"domain-aware-triplet-loss-in-domain","title":"Domain-aware Triplet loss in Domain Generalization","date":"2023-03-01","arxiv_id":"2303.01233","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/domain-aware-triplet-loss-in-domain#ran","syntology_url":"https://syntology.ai/paper/2303.01233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.01233"}},"official":{"repos":["workerbcd/dct"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"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/oamixer-object-aware-mixing-layer-for-vision","slug":"oamixer-object-aware-mixing-layer-for-vision","title":"OAMixer: Object-aware Mixing Layer for Vision Transformers","date":"2022-12-13","arxiv_id":"2212.06595","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/oamixer-object-aware-mixing-layer-for-vision#ran","syntology_url":"https://syntology.ai/paper/2212.06595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.06595"}},"official":{"repos":["alinlab/remixer","alinlab/OAMixer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pasta-proportional-amplitude-spectrum","slug":"pasta-proportional-amplitude-spectrum","title":"PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalization","date":"2022-12-02","arxiv_id":"2212.00979","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/pasta-proportional-amplitude-spectrum#ran","syntology_url":"https://syntology.ai/paper/2212.00979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.00979"}},"official":{"repos":["prithv1/pasta"],"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/learning-dense-object-descriptors-from","slug":"learning-dense-object-descriptors-from","title":"Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization","date":"2022-11-28","arxiv_id":"2211.15059","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-dense-object-descriptors-from#ran","syntology_url":"https://syntology.ai/paper/2211.15059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.15059"}},"official":{"repos":["rehg-lab/dope_selfsup"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["community","official"]}}},{"url":"/paper/roboflow-100-a-rich-multi-domain-object","slug":"roboflow-100-a-rich-multi-domain-object","title":"Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark","date":"2022-11-24","arxiv_id":"2211.13523","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/roboflow-100-a-rich-multi-domain-object#ran","syntology_url":"https://syntology.ai/paper/2211.13523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13523"}},"official":{"repos":["roboflow-ai/roboflow-100-benchmark"],"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/harmonizing-the-object-recognition-strategies","slug":"harmonizing-the-object-recognition-strategies","title":"Harmonizing the object recognition strategies of deep neural networks with humans","date":"2022-11-08","arxiv_id":"2211.04533","repositories_listed":3,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":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) · 2 unverified","sample_list":"/paper/harmonizing-the-object-recognition-strategies#ran","syntology_url":"https://syntology.ai/paper/2211.04533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.04533"}},"official":null}},{"url":"/paper/visual-recognition-with-deep-nearest","slug":"visual-recognition-with-deep-nearest","title":"Visual Recognition with Deep Nearest Centroids","date":"2022-09-15","arxiv_id":"2209.07383","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/visual-recognition-with-deep-nearest#ran","syntology_url":"https://syntology.ai/paper/2209.07383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07383"}},"official":{"repos":["chenghan111/dnc"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/patchwork-fast-and-robust-ground-segmentation","slug":"patchwork-fast-and-robust-ground-segmentation","title":"Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud","date":"2022-07-25","arxiv_id":"2207.11919","repositories_listed":2,"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/patchwork-fast-and-robust-ground-segmentation#ran","syntology_url":"https://syntology.ai/paper/2207.11919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.11919"}},"official":null}},{"url":"/paper/contributions-of-shape-texture-and-color-in","slug":"contributions-of-shape-texture-and-color-in","title":"Contributions of Shape, Texture, and Color in Visual Recognition","date":"2022-07-19","arxiv_id":"2207.09510","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/contributions-of-shape-texture-and-color-in#ran","syntology_url":"https://syntology.ai/paper/2207.09510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09510"}},"official":{"repos":["gyhandy/humanoid-vision-engine"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/sess-saliency-enhancing-with-scaling-and","slug":"sess-saliency-enhancing-with-scaling-and","title":"SESS: Saliency Enhancing with Scaling and Sliding","date":"2022-07-05","arxiv_id":"2207.01769","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/sess-saliency-enhancing-with-scaling-and#ran","syntology_url":"https://syntology.ai/paper/2207.01769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01769"}},"official":{"repos":["neouyghur/sess"],"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/learning-iterative-reasoning-through-energy","slug":"learning-iterative-reasoning-through-energy","title":"Learning Iterative Reasoning through Energy Minimization","date":"2022-06-30","arxiv_id":"2206.15448","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":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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-iterative-reasoning-through-energy#ran","syntology_url":"https://syntology.ai/paper/2206.15448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.15448"}},"official":{"repos":["yilundu/irem_code_release"],"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/sparse-fusion-mixture-of-experts-are-domain","slug":"sparse-fusion-mixture-of-experts-are-domain","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners","date":"2022-06-08","arxiv_id":"2206.04046","repositories_listed":2,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":14,"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) · 5 unverified","sample_list":"/paper/sparse-fusion-mixture-of-experts-are-domain#ran","syntology_url":"https://syntology.ai/paper/2206.04046","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.04046"}},"official":{"repos":["luodian/sf-moe-dg"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/proxymix-proxy-based-mixup-training-with","slug":"proxymix-proxy-based-mixup-training-with","title":"ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation","date":"2022-05-29","arxiv_id":"2205.14566","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/proxymix-proxy-based-mixup-training-with#ran","syntology_url":"https://syntology.ai/paper/2205.14566","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14566"}},"official":{"repos":["yuhed/proxymix"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/the-developmental-trajectory-of-object","slug":"the-developmental-trajectory-of-object","title":"The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks","date":"2022-05-20","arxiv_id":"2205.10144","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/the-developmental-trajectory-of-object#ran","syntology_url":"https://syntology.ai/paper/2205.10144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10144"}},"official":{"repos":["wichmann-lab/robustness-development"],"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/causal-transportability-for-visual","slug":"causal-transportability-for-visual","title":"Causal Transportability for Visual Recognition","date":"2022-04-26","arxiv_id":"2204.12363","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/causal-transportability-for-visual#ran","syntology_url":"https://syntology.ai/paper/2204.12363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12363"}},"official":{"repos":["cvlab-columbia/ct4recognition"],"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/joint-distribution-matters-deep-brownian","slug":"joint-distribution-matters-deep-brownian","title":"Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification","date":"2022-04-09","arxiv_id":"2204.04567","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/joint-distribution-matters-deep-brownian#ran","syntology_url":"https://syntology.ai/paper/2204.04567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.04567"}},"official":{"repos":["Fei-Long121/DeepBDC"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ev-tta-test-time-adaptation-for-event-based","slug":"ev-tta-test-time-adaptation-for-event-based","title":"Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition","date":"2022-03-23","arxiv_id":"2203.12247","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/ev-tta-test-time-adaptation-for-event-based#ran","syntology_url":"https://syntology.ai/paper/2203.12247","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12247"}},"official":{"repos":["82magnolia/ev_tta"],"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/detmatch-two-teachers-are-better-than-one-for","slug":"detmatch-two-teachers-are-better-than-one-for","title":"DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection","date":"2022-03-17","arxiv_id":"2203.09510","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/detmatch-two-teachers-are-better-than-one-for#ran","syntology_url":"https://syntology.ai/paper/2203.09510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09510"}},"official":{"repos":["divadi/detmatch"],"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/dall-eval-probing-the-reasoning-skills-and","slug":"dall-eval-probing-the-reasoning-skills-and","title":"DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models","date":"2022-02-08","arxiv_id":"2202.04053","repositories_listed":2,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dall-eval-probing-the-reasoning-skills-and#ran","syntology_url":"https://syntology.ai/paper/2202.04053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.04053"}},"official":{"repos":["j-min/dalleval"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-the-two-stage-framework-for","slug":"rethinking-the-two-stage-framework-for","title":"Rethinking the Two-Stage Framework for Grounded Situation Recognition","date":"2021-12-10","arxiv_id":"2112.05375","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":3,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":12,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/rethinking-the-two-stage-framework-for#ran","syntology_url":"https://syntology.ai/paper/2112.05375","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.05375"}},"official":{"repos":["kellyiss/situformer"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/tdan-top-down-attention-networks-for-enhanced","slug":"tdan-top-down-attention-networks-for-enhanced","title":"TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs","date":"2021-11-26","arxiv_id":"2111.13470","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/tdan-top-down-attention-networks-for-enhanced#ran","syntology_url":"https://syntology.ai/paper/2111.13470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13470"}},"official":{"repos":["shantanuj/tdam_top_down_attention_module"],"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/evdistill-asynchronous-events-to-end-task-1","slug":"evdistill-asynchronous-events-to-end-task-1","title":"EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation","date":"2021-11-24","arxiv_id":"2111.12341","repositories_listed":1,"syntology":{"n":17,"n_ran":10,"n_constructed":6,"n_ran_checked":6,"n_instrument":4,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":17,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/evdistill-asynchronous-events-to-end-task-1#ran","syntology_url":"https://syntology.ai/paper/2111.12341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.12341"}},"official":{"repos":["addisonwang2013/evdistill"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/mvt-multi-view-vision-transformer-for-3d","slug":"mvt-multi-view-vision-transformer-for-3d","title":"MVT: Multi-view Vision Transformer for 3D Object Recognition","date":"2021-10-25","arxiv_id":"2110.13083","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mvt-multi-view-vision-transformer-for-3d#ran","syntology_url":"https://syntology.ai/paper/2110.13083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.13083"}},"official":{"repos":["shanshuo/MVT"],"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":["listed","official"]}}},{"url":"/paper/iconqa-a-new-benchmark-for-abstract-diagram","slug":"iconqa-a-new-benchmark-for-abstract-diagram","title":"IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning","date":"2021-10-25","arxiv_id":"2110.13214","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"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) · 2 unverified","sample_list":"/paper/iconqa-a-new-benchmark-for-abstract-diagram#ran","syntology_url":"https://syntology.ai/paper/2110.13214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.13214"}},"official":{"repos":["lupantech/iconqa"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mind-your-outliers-investigating-the-negative","slug":"mind-your-outliers-investigating-the-negative","title":"Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering","date":"2021-07-06","arxiv_id":"2107.02331","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mind-your-outliers-investigating-the-negative#ran","syntology_url":"https://syntology.ai/paper/2107.02331","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.02331"}},"official":{"repos":["siddk/vqa-outliers"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/small-in-distribution-changes-in-3d","slug":"small-in-distribution-changes-in-3d","title":"In-distribution adversarial attacks on object recognition models using gradient-free search","date":"2021-06-30","arxiv_id":"2106.16198","repositories_listed":2,"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/small-in-distribution-changes-in-3d#ran","syntology_url":"https://syntology.ai/paper/2106.16198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.16198"}},"official":{"repos":["spandan-madan/in_distribution_adversarial_examples","in-dist-adversarials/in_distribution_adversarial_examples"],"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/self-supervised-learning-with-kernel","slug":"self-supervised-learning-with-kernel","title":"Self-Supervised Learning with Kernel Dependence Maximization","date":"2021-06-15","arxiv_id":"2106.08320","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-learning-with-kernel#ran","syntology_url":"https://syntology.ai/paper/2106.08320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.08320"}},"official":{"repos":["deepmind/ssl_hsic"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/partial-success-in-closing-the-gap-between","slug":"partial-success-in-closing-the-gap-between","title":"Partial success in closing the gap between human and machine vision","date":"2021-06-14","arxiv_id":"2106.07411","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/partial-success-in-closing-the-gap-between#ran","syntology_url":"https://syntology.ai/paper/2106.07411","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07411"}},"official":{"repos":["bethgelab/model-vs-human"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/person-re-identification-with-a-locally-aware","slug":"person-re-identification-with-a-locally-aware","title":"Person Re-Identification with a Locally Aware Transformer","date":"2021-06-07","arxiv_id":"2106.03720","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":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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/person-re-identification-with-a-locally-aware#ran","syntology_url":"https://syntology.ai/paper/2106.03720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03720"}},"official":{"repos":["SiddhantKapil/LA-Transformer"],"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/convolutional-neural-networks-with-gated","slug":"convolutional-neural-networks-with-gated","title":"Convolutional Neural Networks with Gated Recurrent Connections","date":"2021-06-05","arxiv_id":"2106.02859","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/convolutional-neural-networks-with-gated#ran","syntology_url":"https://syntology.ai/paper/2106.02859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02859"}},"official":{"repos":["Jianf-Wang/GRCNN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/doctor-a-simple-method-for-detecting","slug":"doctor-a-simple-method-for-detecting","title":"DOCTOR: A Simple Method for Detecting Misclassification Errors","date":"2021-06-04","arxiv_id":"2106.02395","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":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/doctor-a-simple-method-for-detecting#ran","syntology_url":"https://syntology.ai/paper/2106.02395","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02395"}},"official":{"repos":["doctor-public-submission/DOCTOR"],"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/orbit-a-real-world-few-shot-dataset-for","slug":"orbit-a-real-world-few-shot-dataset-for","title":"ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition","date":"2021-04-08","arxiv_id":"2104.03841","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/orbit-a-real-world-few-shot-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2104.03841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.03841"}},"official":{"repos":["microsoft/ORBIT-Dataset"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/f-siol-310-a-robotic-dataset-and-benchmark","slug":"f-siol-310-a-robotic-dataset-and-benchmark","title":"F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning","date":"2021-03-23","arxiv_id":"2103.12242","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/f-siol-310-a-robotic-dataset-and-benchmark#ran","syntology_url":"https://syntology.ai/paper/2103.12242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12242"}},"official":null}},{"url":"/paper/contemplating-real-world-object","slug":"contemplating-real-world-object","title":"Contemplating real-world object classification","date":"2021-03-08","arxiv_id":"2103.05137","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/contemplating-real-world-object#ran","syntology_url":"https://syntology.ai/paper/2103.05137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05137"}},"official":{"repos":["aliborji/ObjectNetReanalysis"],"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/learning-transferable-visual-models-from","slug":"learning-transferable-visual-models-from","title":"Learning Transferable Visual Models From Natural Language Supervision","date":"2021-02-26","arxiv_id":"2103.00020","repositories_listed":82,"syntology":{"n":20,"n_ran":16,"n_constructed":0,"n_ran_checked":2,"n_instrument":14,"n_unverified":4,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":16,"phrase":"16 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; 14 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-transferable-visual-models-from#ran","syntology_url":"https://syntology.ai/paper/2103.00020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.00020"}},"official":{"repos":["openai/CLIP"],"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/using-shape-to-categorize-low-shot-learning","slug":"using-shape-to-categorize-low-shot-learning","title":"Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias","date":"2021-01-18","arxiv_id":"2101.07296","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/using-shape-to-categorize-low-shot-learning#ran","syntology_url":"https://syntology.ai/paper/2101.07296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.07296"}},"official":null}},{"url":"/paper/self-supervised-pretraining-of-3d-features-on","slug":"self-supervised-pretraining-of-3d-features-on","title":"Self-Supervised Pretraining of 3D Features on any Point-Cloud","date":"2021-01-07","arxiv_id":"2101.02691","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/self-supervised-pretraining-of-3d-features-on#ran","syntology_url":"https://syntology.ai/paper/2101.02691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.02691"}},"official":{"repos":["facebookresearch/DepthContrast"],"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/projected-distribution-loss-for-image","slug":"projected-distribution-loss-for-image","title":"Projected Distribution Loss for Image Enhancement","date":"2020-12-16","arxiv_id":"2012.09289","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/projected-distribution-loss-for-image#ran","syntology_url":"https://syntology.ai/paper/2012.09289","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.09289"}},"official":null}},{"url":"/paper/source-data-absent-unsupervised-domain","slug":"source-data-absent-unsupervised-domain","title":"Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer","date":"2020-12-14","arxiv_id":"2012.07297","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/source-data-absent-unsupervised-domain#ran","syntology_url":"https://syntology.ai/paper/2012.07297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.07297"}},"official":{"repos":["tim-learn/SHOT-plus"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/weakly-supervised-visualbert-pre-training","slug":"weakly-supervised-visualbert-pre-training","title":"Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions","date":"2020-10-24","arxiv_id":"2010.12831","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/weakly-supervised-visualbert-pre-training#ran","syntology_url":"https://syntology.ai/paper/2010.12831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12831"}},"official":null}},{"url":"/paper/offline-meta-reinforcement-learning-with","slug":"offline-meta-reinforcement-learning-with","title":"Offline Meta-Reinforcement Learning with Advantage Weighting","date":"2020-08-13","arxiv_id":"2008.06043","repositories_listed":2,"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":5,"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/offline-meta-reinforcement-learning-with#ran","syntology_url":"https://syntology.ai/paper/2008.06043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.06043"}},"official":{"repos":["eric-mitchell/macaw"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/improving-few-shot-visual-classification-with","slug":"improving-few-shot-visual-classification-with","title":"Enhancing Few-Shot Image Classification with Unlabelled Examples","date":"2020-06-17","arxiv_id":"2006.12245","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"7 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-few-shot-visual-classification-with#ran","syntology_url":"https://syntology.ai/paper/2006.12245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12245"}},"official":{"repos":["plai-group/simple-cnaps"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/unsupervised-domain-adaptation-through-inter","slug":"unsupervised-domain-adaptation-through-inter","title":"Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition","date":"2020-04-21","arxiv_id":"2004.10016","repositories_listed":3,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":5,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 1 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unsupervised-domain-adaptation-through-inter#ran","syntology_url":"https://syntology.ai/paper/2004.10016","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.10016"}},"official":null}},{"url":"/paper/the-notorious-difficulty-of-comparing-human","slug":"the-notorious-difficulty-of-comparing-human","title":"Five Points to Check when Comparing Visual Perception in Humans and Machines","date":"2020-04-20","arxiv_id":"2004.09406","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/the-notorious-difficulty-of-comparing-human#ran","syntology_url":"https://syntology.ai/paper/2004.09406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.09406"}},"official":{"repos":["bethgelab/notorious_difficulty_of_comparing_human_and_machine_perception"],"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/objectnet-dataset-reanalysis-and-correction","slug":"objectnet-dataset-reanalysis-and-correction","title":"ObjectNet Dataset: Reanalysis and Correction","date":"2020-04-04","arxiv_id":"2004.02042","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/objectnet-dataset-reanalysis-and-correction#ran","syntology_url":"https://syntology.ai/paper/2004.02042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.02042"}},"official":{"repos":["aliborji/ObjectNetReanalysis"],"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/attribution-in-scale-and-space","slug":"attribution-in-scale-and-space","title":"Attribution in Scale and Space","date":"2020-04-03","arxiv_id":"2004.03383","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/attribution-in-scale-and-space#ran","syntology_url":"https://syntology.ai/paper/2004.03383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03383"}},"official":{"repos":["PAIR-code/saliency"],"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/egoshots-an-ego-vision-life-logging-dataset","slug":"egoshots-an-ego-vision-life-logging-dataset","title":"Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models","date":"2020-03-26","arxiv_id":"2003.11743","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/egoshots-an-ego-vision-life-logging-dataset#ran","syntology_url":"https://syntology.ai/paper/2003.11743","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.11743"}},"official":{"repos":["NataliaDiaz/Egoshots","Pranav21091996/Semantic_Fidelity-and-Egoshots"],"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/equalization-loss-for-long-tailed-object","slug":"equalization-loss-for-long-tailed-object","title":"Equalization Loss for Long-Tailed Object Recognition","date":"2020-03-11","arxiv_id":"2003.05176","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":12,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/equalization-loss-for-long-tailed-object#ran","syntology_url":"https://syntology.ai/paper/2003.05176","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05176"}},"official":{"repos":["tztztztztz/eql.detectron2"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-framework-for-contrastive-learning","slug":"a-simple-framework-for-contrastive-learning","title":"A Simple Framework for Contrastive Learning of Visual Representations","date":"2020-02-13","arxiv_id":"2002.05709","repositories_listed":96,"syntology":{"n":137,"n_ran":115,"n_constructed":33,"n_ran_checked":90,"n_instrument":25,"n_unverified":22,"n_honours":1,"n_violates":1,"n_no_contract":88,"n_pointer_only":52,"phrase":"115 ran (of which 33 constructed an object rather than computing a result; 90 with no instrument failure: 1 honoured, 1 violated, 88 with no contract checked; 25 where Syntology's instrument failed) · 22 unverified","sample_list":"/paper/a-simple-framework-for-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2002.05709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05709"}},"official":{"repos":["google-research/simclr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/openloris-object-a-dataset-and-benchmark","slug":"openloris-object-a-dataset-and-benchmark","title":"OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning","date":"2019-11-15","arxiv_id":"1911.06487","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/openloris-object-a-dataset-and-benchmark#ran","syntology_url":"https://syntology.ai/paper/1911.06487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06487"}},"official":null}},{"url":"/paper/direct-training-based-spiking-convolutional","slug":"direct-training-based-spiking-convolutional","title":"Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance","date":"2019-09-24","arxiv_id":"1909.10837","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/direct-training-based-spiking-convolutional#ran","syntology_url":"https://syntology.ai/paper/1909.10837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.10837"}},"official":{"repos":["zbs881314/Temporal-Coded-Deep-SNN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/brain-like-object-recognition-with-high","slug":"brain-like-object-recognition-with-high","title":"Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs","date":"2019-09-13","arxiv_id":"1909.06161","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/brain-like-object-recognition-with-high#ran","syntology_url":"https://syntology.ai/paper/1909.06161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.06161"}},"official":{"repos":["dicarlolab/cornet","dicarlolab/neurips2019"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-domain-adaptation-for","slug":"self-supervised-domain-adaptation-for","title":"Self-supervised Domain Adaptation for Computer Vision Tasks","date":"2019-07-25","arxiv_id":"1907.10915","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/self-supervised-domain-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/1907.10915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10915"}},"official":{"repos":["Jiaolong/self-supervised-da"],"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/scenegraphnet-neural-message-passing-for-3d","slug":"scenegraphnet-neural-message-passing-for-3d","title":"SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation","date":"2019-07-25","arxiv_id":"1907.11308","repositories_listed":2,"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/scenegraphnet-neural-message-passing-for-3d#ran","syntology_url":"https://syntology.ai/paper/1907.11308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.11308"}},"official":{"repos":["yzhou359/3DIndoor-SceneGraphNet"],"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/learning-classifier-synthesis-for-generalized","slug":"learning-classifier-synthesis-for-generalized","title":"Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning","date":"2019-06-07","arxiv_id":"1906.02944","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-classifier-synthesis-for-generalized#ran","syntology_url":"https://syntology.ai/paper/1906.02944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02944"}},"official":{"repos":["Sha-Lab/CASTLE"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-learning-from-video-with-deep","slug":"unsupervised-learning-from-video-with-deep","title":"Unsupervised Learning from Video with Deep Neural Embeddings","date":"2019-05-28","arxiv_id":"1905.11954","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-learning-from-video-with-deep#ran","syntology_url":"https://syntology.ai/paper/1905.11954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.11954"}},"official":{"repos":["neuroailab/VIE"],"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/interpreting-adversarially-trained","slug":"interpreting-adversarially-trained","title":"Interpreting Adversarially Trained Convolutional Neural Networks","date":"2019-05-23","arxiv_id":"1905.09797","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/interpreting-adversarially-trained#ran","syntology_url":"https://syntology.ai/paper/1905.09797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.09797"}},"official":{"repos":["PKUAI26/AT-CNN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sparse-transfer-learning-via-winning-lottery","slug":"sparse-transfer-learning-via-winning-lottery","title":"Sparse Transfer Learning via Winning Lottery Tickets","date":"2019-05-19","arxiv_id":"1905.07785","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sparse-transfer-learning-via-winning-lottery#ran","syntology_url":"https://syntology.ai/paper/1905.07785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.07785"}},"official":{"repos":["rahulsmehta/sparsity-experiments"],"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/end-to-end-learning-of-representations-for","slug":"end-to-end-learning-of-representations-for","title":"End-to-End Learning of Representations for Asynchronous Event-Based Data","date":"2019-04-17","arxiv_id":"1904.08245","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":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/end-to-end-learning-of-representations-for#ran","syntology_url":"https://syntology.ai/paper/1904.08245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08245"}},"official":{"repos":["uzh-rpg/rpg_event_representation_learning"],"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/understanding-and-visualizing-deep-visual","slug":"understanding-and-visualizing-deep-visual","title":"Understanding and Visualizing Deep Visual Saliency Models","date":"2019-03-06","arxiv_id":"1903.02501","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/understanding-and-visualizing-deep-visual#ran","syntology_url":"https://syntology.ai/paper/1903.02501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02501"}},"official":{"repos":["SenHe/uavdvsm"],"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/factorized-attention-self-attention-with","slug":"factorized-attention-self-attention-with","title":"Efficient Attention: Attention with Linear Complexities","date":"2018-12-04","arxiv_id":"1812.01243","repositories_listed":14,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":4,"n_no_contract":1,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 4 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/factorized-attention-self-attention-with#ran","syntology_url":"https://syntology.ai/paper/1812.01243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01243"}},"official":{"repos":["cmsflash/efficient-attention"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"8e0cf11797e494144f2b7f41e91ea2da2dfaf662b7e6509e1f462d38703f428e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}