{"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":"/dataset/coco/papers/ran/2","list_of":"/dataset/coco","dataset":"COCO (Common Objects in Context)","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_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'","samples_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)"},"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 dataset or check it against this dataset'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.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":2,"pages_in_order":3,"rows_per_page":100,"rows":[101,200],"of":256,"counts":{"papers_with_a_benchmark_row":579,"with_a_code_link":504,"where_syntology_ran_a_sample":256,"not_listed_spam_title":0,"listed":579,"listed_where_code_ran":256,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":226,"every_run_a_failure_of_syntologys_instrument":30,"listed_with_a_run_with_no_instrument_failure":226,"listed_every_run_a_failure_of_syntologys_instrument":30,"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 with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/coco/papers/ran/1","prev":"/dataset/coco/papers/ran/1","next":"/dataset/coco/papers/ran/3","papers":[{"paper":"/paper/truncated-diffusion-probabilistic-models","slug":"truncated-diffusion-probabilistic-models","title":"Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders","date":"2022-02-19","arxiv_id":"2202.09671","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["jegzheng/truncated-diffusion-probabilistic-models"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/truncated-diffusion-probabilistic-models#ran","syntology_url":"https://syntology.ai/paper/2202.09671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.09671"}}}},{"paper":"/paper/dab-detr-dynamic-anchor-boxes-are-better-1","slug":"dab-detr-dynamic-anchor-boxes-are-better-1","title":"DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR","date":"2022-01-28","arxiv_id":"2201.12329","rows_on_this_dataset":2,"code_links":8,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":6,"samples_constructed":3,"samples_ran_checked":4,"samples_ran_instrument_failed":2,"samples_unverified":5,"pointer_only_for_licence":0,"official":{"repos":["slongliu/dab-detr"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/dab-detr-dynamic-anchor-boxes-are-better-1#ran","syntology_url":"https://syntology.ai/paper/2201.12329","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12329"}}}},{"paper":"/paper/when-shift-operation-meets-vision-transformer","slug":"when-shift-operation-meets-vision-transformer","title":"When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism","date":"2022-01-26","arxiv_id":"2201.10801","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":6,"samples_constructed":6,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["microsoft/SPACH"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/when-shift-operation-meets-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2201.10801","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.10801"}}}},{"paper":"/paper/vision-transformer-with-deformable-attention","slug":"vision-transformer-with-deformable-attention","title":"Vision Transformer with Deformable Attention","date":"2022-01-03","arxiv_id":"2201.00520","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":8,"samples_constructed":4,"samples_ran_checked":4,"samples_ran_instrument_failed":4,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["leaplabthu/dat"],"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":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/vision-transformer-with-deformable-attention#ran","syntology_url":"https://syntology.ai/paper/2201.00520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.00520"}}}},{"paper":"/paper/robust-region-feature-synthesizer-for-zero","slug":"robust-region-feature-synthesizer-for-zero","title":"Robust Region Feature Synthesizer for Zero-Shot Object Detection","date":"2022-01-01","arxiv_id":"2201.00103","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":1,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":2,"official":{"repos":["HPL123/RRFS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/robust-region-feature-synthesizer-for-zero#ran","syntology_url":"https://syntology.ai/paper/2201.00103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.00103"}}}},{"paper":"/paper/augmenting-convolutional-networks-with","slug":"augmenting-convolutional-networks-with","title":"Augmenting Convolutional networks with attention-based aggregation","date":"2021-12-27","arxiv_id":"2112.13692","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["facebookresearch/deit"],"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":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/augmenting-convolutional-networks-with#ran","syntology_url":"https://syntology.ai/paper/2112.13692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13692"}}}},{"paper":"/paper/elsa-enhanced-local-self-attention-for-vision","slug":"elsa-enhanced-local-self-attention-for-vision","title":"ELSA: Enhanced Local Self-Attention for Vision Transformer","date":"2021-12-23","arxiv_id":"2112.12786","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["damo-cv/elsa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/elsa-enhanced-local-self-attention-for-vision#ran","syntology_url":"https://syntology.ai/paper/2112.12786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.12786"}}}},{"paper":"/paper/glide-towards-photorealistic-image-generation","slug":"glide-towards-photorealistic-image-generation","title":"GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models","date":"2021-12-20","arxiv_id":"2112.10741","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":15,"samples_ran":9,"samples_constructed":7,"samples_ran_checked":8,"samples_ran_instrument_failed":1,"samples_unverified":6,"pointer_only_for_licence":0,"official":{"repos":["openai/glide-text2im"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/glide-towards-photorealistic-image-generation#ran","syntology_url":"https://syntology.ai/paper/2112.10741","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.10741"}}}},{"paper":"/paper/high-resolution-image-synthesis-with-latent","slug":"high-resolution-image-synthesis-with-latent","title":"High-Resolution Image Synthesis with Latent Diffusion Models","date":"2021-12-20","arxiv_id":"2112.10752","rows_on_this_dataset":3,"code_links":41,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":28,"samples_ran":22,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":12,"samples_unverified":6,"pointer_only_for_licence":10,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/high-resolution-image-synthesis-with-latent#ran","syntology_url":"https://syntology.ai/paper/2112.10752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.10752"}}}},{"paper":"/paper/grounded-language-image-pre-training","slug":"grounded-language-image-pre-training","title":"Grounded Language-Image Pre-training","date":"2021-12-07","arxiv_id":"2112.03857","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["microsoft/GLIP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/grounded-language-image-pre-training#ran","syntology_url":"https://syntology.ai/paper/2112.03857","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.03857"}}}},{"paper":"/paper/masked-attention-mask-transformer-for","slug":"masked-attention-mask-transformer-for","title":"Masked-attention Mask Transformer for Universal Image Segmentation","date":"2021-12-02","arxiv_id":"2112.01527","rows_on_this_dataset":6,"code_links":7,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["facebookresearch/Mask2Former"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/masked-attention-mask-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2112.01527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.01527"}}}},{"paper":"/paper/lafite-towards-language-free-training-for","slug":"lafite-towards-language-free-training-for","title":"LAFITE: Towards Language-Free Training for Text-to-Image Generation","date":"2021-11-27","arxiv_id":"2111.13792","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":18,"samples_ran":12,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":2,"samples_unverified":6,"pointer_only_for_licence":3,"official":{"repos":["drboog/Lafite"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/lafite-towards-language-free-training-for#ran","syntology_url":"https://syntology.ai/paper/2111.13792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13792"}}}},{"paper":"/paper/mask-transfiner-for-high-quality-instance","slug":"mask-transfiner-for-high-quality-instance","title":"Mask Transfiner for High-Quality Instance Segmentation","date":"2021-11-26","arxiv_id":"2111.13673","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["SysCV/transfiner"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/mask-transfiner-for-high-quality-instance#ran","syntology_url":"https://syntology.ai/paper/2111.13673","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13673"}}}},{"paper":"/paper/ml-decoder-scalable-and-versatile","slug":"ml-decoder-scalable-and-versatile","title":"ML-Decoder: Scalable and Versatile Classification Head","date":"2021-11-25","arxiv_id":"2111.12933","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["alibaba-miil/ml_decoder"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ml-decoder-scalable-and-versatile#ran","syntology_url":"https://syntology.ai/paper/2111.12933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.12933"}}}},{"paper":"/paper/nuwa-visual-synthesis-pre-training-for-neural","slug":"nuwa-visual-synthesis-pre-training-for-neural","title":"NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion","date":"2021-11-24","arxiv_id":"2111.12417","rows_on_this_dataset":7,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":2,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/nuwa-visual-synthesis-pre-training-for-neural#ran","syntology_url":"https://syntology.ai/paper/2111.12417","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.12417"}}}},{"paper":"/paper/metaformer-is-actually-what-you-need-for","slug":"metaformer-is-actually-what-you-need-for","title":"MetaFormer Is Actually What You Need for Vision","date":"2021-11-22","arxiv_id":"2111.11418","rows_on_this_dataset":1,"code_links":18,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["rwightman/pytorch-image-models","sail-sg/poolformer"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/metaformer-is-actually-what-you-need-for#ran","syntology_url":"https://syntology.ai/paper/2111.11418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.11418"}}}},{"paper":"/paper/swin-transformer-v2-scaling-up-capacity-and","slug":"swin-transformer-v2-scaling-up-capacity-and","title":"Swin Transformer V2: Scaling Up Capacity and Resolution","date":"2021-11-18","arxiv_id":"2111.09883","rows_on_this_dataset":4,"code_links":23,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":30,"samples_ran":17,"samples_constructed":0,"samples_ran_checked":17,"samples_ran_instrument_failed":0,"samples_unverified":13,"pointer_only_for_licence":4,"official":{"repos":["microsoft/Swin-Transformer"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/swin-transformer-v2-scaling-up-capacity-and#ran","syntology_url":"https://syntology.ai/paper/2111.09883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09883"}}}},{"paper":"/paper/multi-grained-vision-language-pre-training","slug":"multi-grained-vision-language-pre-training","title":"Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts","date":"2021-11-16","arxiv_id":"2111.08276","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["zengyan-97/x-vlm"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/multi-grained-vision-language-pre-training#ran","syntology_url":"https://syntology.ai/paper/2111.08276","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.08276"}}}},{"paper":"/paper/masked-autoencoders-are-scalable-vision","slug":"masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","arxiv_id":"2111.06377","rows_on_this_dataset":2,"code_links":58,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":137,"samples_ran":86,"samples_constructed":40,"samples_ran_checked":69,"samples_ran_instrument_failed":17,"samples_unverified":51,"pointer_only_for_licence":78,"official":{"repos":["facebookresearch/mae"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/masked-autoencoders-are-scalable-vision#ran","syntology_url":"https://syntology.ai/paper/2111.06377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.06377"}}}},{"paper":"/paper/an-empirical-study-of-training-end-to-end","slug":"an-empirical-study-of-training-end-to-end","title":"An Empirical Study of Training End-to-End Vision-and-Language Transformers","date":"2021-11-03","arxiv_id":"2111.02387","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["zdou0830/meter"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/an-empirical-study-of-training-end-to-end#ran","syntology_url":"https://syntology.ai/paper/2111.02387","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.02387"}}}},{"paper":"/paper/hrformer-high-resolution-transformer-for","slug":"hrformer-high-resolution-transformer-for","title":"HRFormer: High-Resolution Transformer for Dense Prediction","date":"2021-10-18","arxiv_id":"2110.09408","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":7,"samples_constructed":7,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":0,"official":{"repos":["HRNet/HRFormer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/hrformer-high-resolution-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2110.09408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.09408"}}}},{"paper":"/paper/transformer-based-dual-relation-graph-for-1","slug":"transformer-based-dual-relation-graph-for-1","title":"Transformer-based Dual Relation Graph for Multi-label Image Recognition","date":"2021-10-10","arxiv_id":"2110.04722","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":9,"samples_constructed":6,"samples_ran_checked":8,"samples_ran_instrument_failed":1,"samples_unverified":3,"pointer_only_for_licence":0,"official":{"repos":["iCVTEAM/TDRG"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":6,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/transformer-based-dual-relation-graph-for-1#ran","syntology_url":"https://syntology.ai/paper/2110.04722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.04722"}}}},{"paper":"/paper/pix2seq-a-language-modeling-framework-for","slug":"pix2seq-a-language-modeling-framework-for","title":"Pix2seq: A Language Modeling Framework for Object Detection","date":"2021-09-22","arxiv_id":"2109.10852","rows_on_this_dataset":6,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":10,"samples_constructed":3,"samples_ran_checked":6,"samples_ran_instrument_failed":4,"samples_unverified":2,"pointer_only_for_licence":10,"official":{"repos":["google-research/pix2seq"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/pix2seq-a-language-modeling-framework-for#ran","syntology_url":"https://syntology.ai/paper/2109.10852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.10852"}}}},{"paper":"/paper/tood-task-aligned-one-stage-object-detection","slug":"tood-task-aligned-one-stage-object-detection","title":"TOOD: Task-aligned One-stage Object Detection","date":"2021-08-17","arxiv_id":"2108.07755","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":3,"official":{"repos":["fcjian/TOOD"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/tood-task-aligned-one-stage-object-detection#ran","syntology_url":"https://syntology.ai/paper/2108.07755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07755"}}}},{"paper":"/paper/conditional-detr-for-fast-training","slug":"conditional-detr-for-fast-training","title":"Conditional DETR for Fast Training Convergence","date":"2021-08-13","arxiv_id":"2108.06152","rows_on_this_dataset":4,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":6,"samples_constructed":1,"samples_ran_checked":4,"samples_ran_instrument_failed":2,"samples_unverified":3,"pointer_only_for_licence":4,"official":{"repos":["atten4vis/conditionaldetr"],"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":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/conditional-detr-for-fast-training#ran","syntology_url":"https://syntology.ai/paper/2108.06152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06152"}}}},{"paper":"/paper/rethinking-and-improving-relative-position","slug":"rethinking-and-improving-relative-position","title":"Rethinking and Improving Relative Position Encoding for Vision Transformer","date":"2021-07-29","arxiv_id":"2107.14222","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":7,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["microsoft/cream"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/rethinking-and-improving-relative-position#ran","syntology_url":"https://syntology.ai/paper/2107.14222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.14222"}}}},{"paper":"/paper/query2label-a-simple-transformer-way-to-multi","slug":"query2label-a-simple-transformer-way-to-multi","title":"Query2Label: A Simple Transformer Way to Multi-Label Classification","date":"2021-07-22","arxiv_id":"2107.10834","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["SlongLiu/query2labels"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/query2label-a-simple-transformer-way-to-multi#ran","syntology_url":"https://syntology.ai/paper/2107.10834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.10834"}}}},{"paper":"/paper/yolox-exceeding-yolo-series-in-2021","slug":"yolox-exceeding-yolo-series-in-2021","title":"YOLOX: Exceeding YOLO Series in 2021","date":"2021-07-18","arxiv_id":"2107.08430","rows_on_this_dataset":4,"code_links":42,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":23,"samples_ran":16,"samples_constructed":0,"samples_ran_checked":15,"samples_ran_instrument_failed":1,"samples_unverified":7,"pointer_only_for_licence":0,"official":{"repos":["Megvii-BaseDetection/YOLOX"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/yolox-exceeding-yolo-series-in-2021#ran","syntology_url":"https://syntology.ai/paper/2107.08430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.08430"}}}},{"paper":"/paper/align-before-fuse-vision-and-language","slug":"align-before-fuse-vision-and-language","title":"Align before Fuse: Vision and Language Representation Learning with Momentum Distillation","date":"2021-07-16","arxiv_id":"2107.07651","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":1,"pointer_only_for_licence":3,"official":{"repos":["salesforce/lavis"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/align-before-fuse-vision-and-language#ran","syntology_url":"https://syntology.ai/paper/2107.07651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07651"}}}},{"paper":"/paper/cbnetv2-a-composite-backbone-network","slug":"cbnetv2-a-composite-backbone-network","title":"CBNet: A Composite Backbone Network Architecture for Object Detection","date":"2021-07-01","arxiv_id":"2107.00420","rows_on_this_dataset":9,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":2,"official":{"repos":["VDIGPKU/CBNetV2"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/cbnetv2-a-composite-backbone-network#ran","syntology_url":"https://syntology.ai/paper/2107.00420","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00420"}}}},{"paper":"/paper/focal-self-attention-for-local-global","slug":"focal-self-attention-for-local-global","title":"Focal Self-attention for Local-Global Interactions in Vision Transformers","date":"2021-07-01","arxiv_id":"2107.00641","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["microsoft/Focal-Transformer"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/focal-self-attention-for-local-global#ran","syntology_url":"https://syntology.ai/paper/2107.00641","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00641"}}}},{"paper":"/paper/k-net-towards-unified-image-segmentation","slug":"k-net-towards-unified-image-segmentation","title":"K-Net: Towards Unified Image Segmentation","date":"2021-06-28","arxiv_id":"2106.14855","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["zwwwayne/k-net"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/k-net-towards-unified-image-segmentation#ran","syntology_url":"https://syntology.ai/paper/2106.14855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.14855"}}}},{"paper":"/paper/xcit-cross-covariance-image-transformers","slug":"xcit-cross-covariance-image-transformers","title":"XCiT: Cross-Covariance Image Transformers","date":"2021-06-17","arxiv_id":"2106.09681","rows_on_this_dataset":4,"code_links":12,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":14,"samples_ran":10,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":11,"official":{"repos":["facebookresearch/xcit","rwightman/pytorch-image-models"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/xcit-cross-covariance-image-transformers#ran","syntology_url":"https://syntology.ai/paper/2106.09681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09681"}}}},{"paper":"/paper/dynamic-head-unifying-object-detection-heads","slug":"dynamic-head-unifying-object-detection-heads","title":"Dynamic Head: Unifying Object Detection Heads with Attentions","date":"2021-06-15","arxiv_id":"2106.08322","rows_on_this_dataset":9,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":8,"samples_constructed":5,"samples_ran_checked":6,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["microsoft/DynamicHead"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/dynamic-head-unifying-object-detection-heads#ran","syntology_url":"https://syntology.ai/paper/2106.08322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.08322"}}}},{"paper":"/paper/detreg-unsupervised-pretraining-with-region","slug":"detreg-unsupervised-pretraining-with-region","title":"DETReg: Unsupervised Pretraining with Region Priors for Object Detection","date":"2021-06-08","arxiv_id":"2106.04550","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":1,"official":{"repos":["amirbar/detreg"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/detreg-unsupervised-pretraining-with-region#ran","syntology_url":"https://syntology.ai/paper/2106.04550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04550"}}}},{"paper":"/paper/learning-relation-alignment-for-calibrated","slug":"learning-relation-alignment-for-calibrated","title":"Learning Relation Alignment for Calibrated Cross-modal Retrieval","date":"2021-05-28","arxiv_id":"2105.13868","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["lancopku/IAIS"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-relation-alignment-for-calibrated#ran","syntology_url":"https://syntology.ai/paper/2105.13868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.13868"}}}},{"paper":"/paper/cogview-mastering-text-to-image-generation","slug":"cogview-mastering-text-to-image-generation","title":"CogView: Mastering Text-to-Image Generation via Transformers","date":"2021-05-26","arxiv_id":"2105.13290","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["THUDM/CogView"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/cogview-mastering-text-to-image-generation#ran","syntology_url":"https://syntology.ai/paper/2105.13290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.13290"}}}},{"paper":"/paper/vipnas-efficient-video-pose-estimation-via","slug":"vipnas-efficient-video-pose-estimation-via","title":"ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search","date":"2021-05-21","arxiv_id":"2105.10154","rows_on_this_dataset":2,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["luminxu/ViPNAS"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/vipnas-efficient-video-pose-estimation-via#ran","syntology_url":"https://syntology.ai/paper/2105.10154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.10154"}}}},{"paper":"/paper/queryinst-parallelly-supervised-mask-query","slug":"queryinst-parallelly-supervised-mask-query","title":"Instances as Queries","date":"2021-05-05","arxiv_id":"2105.01928","rows_on_this_dataset":4,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["hustvl/QueryInst"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/queryinst-parallelly-supervised-mask-query#ran","syntology_url":"https://syntology.ai/paper/2105.01928","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.01928"}}}},{"paper":"/paper/imagenet-21k-pretraining-for-the-masses","slug":"imagenet-21k-pretraining-for-the-masses","title":"ImageNet-21K Pretraining for the Masses","date":"2021-04-22","arxiv_id":"2104.10972","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":2,"official":{"repos":["Alibaba-MIIL/ImageNet21K"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/imagenet-21k-pretraining-for-the-masses#ran","syntology_url":"https://syntology.ai/paper/2104.10972","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.10972"}}}},{"paper":"/paper/lite-hrnet-a-lightweight-high-resolution","slug":"lite-hrnet-a-lightweight-high-resolution","title":"Lite-HRNet: A Lightweight High-Resolution Network","date":"2021-04-13","arxiv_id":"2104.06403","rows_on_this_dataset":2,"code_links":17,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":29,"samples_ran":13,"samples_constructed":2,"samples_ran_checked":11,"samples_ran_instrument_failed":2,"samples_unverified":16,"pointer_only_for_licence":13,"official":{"repos":["HRNet/Lite-HRNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/lite-hrnet-a-lightweight-high-resolution#ran","syntology_url":"https://syntology.ai/paper/2104.06403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.06403"}}}},{"paper":"/paper/multiple-instance-active-learning-for-object","slug":"multiple-instance-active-learning-for-object","title":"Multiple instance active learning for object detection","date":"2021-04-06","arxiv_id":"2104.02324","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["yuantn/MI-AOD"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/multiple-instance-active-learning-for-object#ran","syntology_url":"https://syntology.ai/paper/2104.02324","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02324"}}}},{"paper":"/paper/weakly-supervised-instance-segmentation-via","slug":"weakly-supervised-instance-segmentation-via","title":"Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images","date":"2021-04-04","arxiv_id":"2104.01526","rows_on_this_dataset":1,"code_links":0,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/weakly-supervised-instance-segmentation-via#ran","syntology_url":"https://syntology.ai/paper/2104.01526","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.01526"}}}},{"paper":"/paper/2103-15358","slug":"2103-15358","title":"Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding","date":"2021-03-29","arxiv_id":"2103.15358","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":2,"official":{"repos":["microsoft/vision-longformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/2103-15358#ran","syntology_url":"https://syntology.ai/paper/2103.15358","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15358"}}}},{"paper":"/paper/ota-optimal-transport-assignment-for-object","slug":"ota-optimal-transport-assignment-for-object","title":"OTA: Optimal Transport Assignment for Object Detection","date":"2021-03-26","arxiv_id":"2103.14259","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":1,"samples_ran_checked":2,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["Megvii-BaseDetection/OTA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ota-optimal-transport-assignment-for-object#ran","syntology_url":"https://syntology.ai/paper/2103.14259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14259"}}}},{"paper":"/paper/swin-transformer-hierarchical-vision","slug":"swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","arxiv_id":"2103.14030","rows_on_this_dataset":8,"code_links":80,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":207,"samples_ran":123,"samples_constructed":45,"samples_ran_checked":82,"samples_ran_instrument_failed":41,"samples_unverified":84,"pointer_only_for_licence":45,"official":{"repos":["microsoft/Swin-Transformer"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/swin-transformer-hierarchical-vision#ran","syntology_url":"https://syntology.ai/paper/2103.14030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14030"}}}},{"paper":"/paper/deep-occlusion-aware-instance-segmentation","slug":"deep-occlusion-aware-instance-segmentation","title":"Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers","date":"2021-03-23","arxiv_id":"2103.12340","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":9,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":5,"official":{"repos":["lkeab/BCNet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/deep-occlusion-aware-instance-segmentation#ran","syntology_url":"https://syntology.ai/paper/2103.12340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12340"}}}},{"paper":"/paper/meta-detr-few-shot-object-detection-via","slug":"meta-detr-few-shot-object-detection-via","title":"Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation","date":"2021-03-22","arxiv_id":"2103.11731","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["ZhangGongjie/Meta-DETR"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/meta-detr-few-shot-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2103.11731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.11731"}}}},{"paper":"/paper/fsce-few-shot-object-detection-via","slug":"fsce-few-shot-object-detection-via","title":"FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding","date":"2021-03-10","arxiv_id":"2103.05950","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["MegviiDetection/FSCE"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/fsce-few-shot-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2103.05950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05950"}}}},{"paper":"/paper/beyond-max-margin-class-margin-equilibrium","slug":"beyond-max-margin-class-margin-equilibrium","title":"Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection","date":"2021-03-08","arxiv_id":"2103.04612","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":6,"official":{"repos":["Bohao-Lee/CME"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/beyond-max-margin-class-margin-equilibrium#ran","syntology_url":"https://syntology.ai/paper/2103.04612","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.04612"}}}},{"paper":"/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","rows_on_this_dataset":2,"code_links":82,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":20,"samples_ran":16,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":14,"samples_unverified":4,"pointer_only_for_licence":16,"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","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"}}}},{"paper":"/paper/pyramid-vision-transformer-a-versatile","slug":"pyramid-vision-transformer-a-versatile","title":"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions","date":"2021-02-24","arxiv_id":"2102.12122","rows_on_this_dataset":2,"code_links":11,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":30,"samples_ran":22,"samples_constructed":16,"samples_ran_checked":18,"samples_ran_instrument_failed":4,"samples_unverified":8,"pointer_only_for_licence":1,"official":{"repos":["whai362/PVT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/pyramid-vision-transformer-a-versatile#ran","syntology_url":"https://syntology.ai/paper/2102.12122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12122"}}}},{"paper":"/paper/scaling-up-visual-and-vision-language","slug":"scaling-up-visual-and-vision-language","title":"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision","date":"2021-02-11","arxiv_id":"2102.05918","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":8,"samples_constructed":6,"samples_ran_checked":7,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":9,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/scaling-up-visual-and-vision-language#ran","syntology_url":"https://syntology.ai/paper/2102.05918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.05918"}}}},{"paper":"/paper/vilt-vision-and-language-transformer-without","slug":"vilt-vision-and-language-transformer-without","title":"ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision","date":"2021-02-05","arxiv_id":"2102.03334","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":1,"samples_constructed":1,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":1,"official":{"repos":["dandelin/vilt"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/vilt-vision-and-language-transformer-without#ran","syntology_url":"https://syntology.ai/paper/2102.03334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.03334"}}}},{"paper":"/paper/multi-hypothesis-pose-networks-rethinking-top","slug":"multi-hypothesis-pose-networks-rethinking-top","title":"Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation","date":"2021-01-27","arxiv_id":"2101.11223","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":1,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["rawalkhirodkar/MIPNet"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/multi-hypothesis-pose-networks-rethinking-top#ran","syntology_url":"https://syntology.ai/paper/2101.11223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.11223"}}}},{"paper":"/paper/bottleneck-transformers-for-visual","slug":"bottleneck-transformers-for-visual","title":"Bottleneck Transformers for Visual Recognition","date":"2021-01-27","arxiv_id":"2101.11605","rows_on_this_dataset":6,"code_links":13,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":49,"samples_ran":26,"samples_constructed":9,"samples_ran_checked":19,"samples_ran_instrument_failed":7,"samples_unverified":23,"pointer_only_for_licence":8,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/bottleneck-transformers-for-visual#ran","syntology_url":"https://syntology.ai/paper/2101.11605","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.11605"}}}},{"paper":"/paper/cross-modal-contrastive-learning-for-text-to","slug":"cross-modal-contrastive-learning-for-text-to","title":"Cross-Modal Contrastive Learning for Text-to-Image Generation","date":"2021-01-12","arxiv_id":"2101.04702","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":4,"official":{"repos":["google-research/xmcgan_image_generation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/cross-modal-contrastive-learning-for-text-to#ran","syntology_url":"https://syntology.ai/paper/2101.04702","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.04702"}}}},{"paper":"/paper/similarity-reasoning-and-filtration-for-image","slug":"similarity-reasoning-and-filtration-for-image","title":"Similarity Reasoning and Filtration for Image-Text Matching","date":"2021-01-05","arxiv_id":"2101.01368","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":10,"samples_constructed":6,"samples_ran_checked":7,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":12,"official":{"repos":["Paranioar/SGRAF"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/similarity-reasoning-and-filtration-for-image#ran","syntology_url":"https://syntology.ai/paper/2101.01368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.01368"}}}},{"paper":"/paper/simple-copy-paste-is-a-strong-data","slug":"simple-copy-paste-is-a-strong-data","title":"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation","date":"2020-12-13","arxiv_id":"2012.07177","rows_on_this_dataset":8,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["tensorflow/tpu"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/simple-copy-paste-is-a-strong-data#ran","syntology_url":"https://syntology.ai/paper/2012.07177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.07177"}}}},{"paper":"/paper/torchdistill-a-modular-configuration-driven","slug":"torchdistill-a-modular-configuration-driven","title":"torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation","date":"2020-11-25","arxiv_id":"2011.12913","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":26,"samples_ran":10,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":10,"samples_unverified":16,"pointer_only_for_licence":0,"official":{"repos":["yoshitomo-matsubara/torchdistill"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":16,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/torchdistill-a-modular-configuration-driven#ran","syntology_url":"https://syntology.ai/paper/2011.12913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.12913"}}}},{"paper":"/paper/evopose2d-pushing-the-boundaries-of-2d-human","slug":"evopose2d-pushing-the-boundaries-of-2d-human","title":"EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight Transfer","date":"2020-11-17","arxiv_id":"2011.08446","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["wmcnally/evopose2d"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/evopose2d-pushing-the-boundaries-of-2d-human#ran","syntology_url":"https://syntology.ai/paper/2011.08446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.08446"}}}},{"paper":"/paper/scaled-yolov4-scaling-cross-stage-partial","slug":"scaled-yolov4-scaling-cross-stage-partial","title":"Scaled-YOLOv4: Scaling Cross Stage Partial Network","date":"2020-11-16","arxiv_id":"2011.08036","rows_on_this_dataset":6,"code_links":41,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["WongKinYiu/ScaledYOLOv4"],"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":["unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/scaled-yolov4-scaling-cross-stage-partial#ran","syntology_url":"https://syntology.ai/paper/2011.08036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.08036"}}}},{"paper":"/paper/in-defense-of-feature-mimicking-for-knowledge","slug":"in-defense-of-feature-mimicking-for-knowledge","title":"Distilling Knowledge by Mimicking Features","date":"2020-11-03","arxiv_id":"2011.01424","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":4,"samples_unverified":1,"pointer_only_for_licence":6,"official":{"repos":["DoctorKey/LSHFM.detection","DoctorKey/LSHFM.multiclassification","DoctorKey/LSHFM.singleclassification"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/in-defense-of-feature-mimicking-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/2011.01424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01424"}}}},{"paper":"/paper/relationnet-bridging-visual-representations","slug":"relationnet-bridging-visual-representations","title":"RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder","date":"2020-10-29","arxiv_id":"2010.15831","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["microsoft/RelationNet2"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/relationnet-bridging-visual-representations#ran","syntology_url":"https://syntology.ai/paper/2010.15831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.15831"}}}},{"paper":"/paper/deformable-detr-deformable-transformers-for-1","slug":"deformable-detr-deformable-transformers-for-1","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","date":"2020-10-08","arxiv_id":"2010.04159","rows_on_this_dataset":1,"code_links":20,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":55,"samples_ran":37,"samples_constructed":9,"samples_ran_checked":24,"samples_ran_instrument_failed":13,"samples_unverified":18,"pointer_only_for_licence":21,"official":{"repos":["fundamentalvision/Deformable-DETR"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/deformable-detr-deformable-transformers-for-1#ran","syntology_url":"https://syntology.ai/paper/2010.04159","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04159"}}}},{"paper":"/paper/asymmetric-loss-for-multi-label","slug":"asymmetric-loss-for-multi-label","title":"Asymmetric Loss For Multi-Label Classification","date":"2020-09-29","arxiv_id":"2009.14119","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":10,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":9,"official":{"repos":["Alibaba-MIIL/ASL"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/asymmetric-loss-for-multi-label#ran","syntology_url":"https://syntology.ai/paper/2009.14119","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14119"}}}},{"paper":"/paper/a-ranking-based-balanced-loss-function","slug":"a-ranking-based-balanced-loss-function","title":"A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection","date":"2020-09-28","arxiv_id":"2009.13592","rows_on_this_dataset":7,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":2,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["kemaloksuz/aLRPLoss"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/a-ranking-based-balanced-loss-function#ran","syntology_url":"https://syntology.ai/paper/2009.13592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13592"}}}},{"paper":"/paper/few-shot-object-detection-and-viewpoint","slug":"few-shot-object-detection-and-viewpoint","title":"Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild","date":"2020-07-23","arxiv_id":"2007.12107","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":17,"samples_ran":15,"samples_constructed":0,"samples_ran_checked":14,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/few-shot-object-detection-and-viewpoint#ran","syntology_url":"https://syntology.ai/paper/2007.12107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12107"}}}},{"paper":"/paper/multi-scale-positive-sample-refinement-for","slug":"multi-scale-positive-sample-refinement-for","title":"Multi-Scale Positive Sample Refinement for Few-Shot Object Detection","date":"2020-07-18","arxiv_id":"2007.09384","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":4,"samples_unverified":2,"pointer_only_for_licence":1,"official":{"repos":["jiaxi-wu/MPSR"],"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":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/multi-scale-positive-sample-refinement-for#ran","syntology_url":"https://syntology.ai/paper/2007.09384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09384"}}}},{"paper":"/paper/virtex-learning-visual-representations-from","slug":"virtex-learning-visual-representations-from","title":"VirTex: Learning Visual Representations from Textual Annotations","date":"2020-06-11","arxiv_id":"2006.06666","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["kdexd/virtex"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/virtex-learning-visual-representations-from#ran","syntology_url":"https://syntology.ai/paper/2006.06666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06666"}}}},{"paper":"/paper/generalized-focal-loss-learning-qualified-and","slug":"generalized-focal-loss-learning-qualified-and","title":"Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection","date":"2020-06-08","arxiv_id":"2006.04388","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":24,"samples_ran":22,"samples_constructed":0,"samples_ran_checked":22,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":2,"official":{"repos":["implus/GFocal"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/generalized-focal-loss-learning-qualified-and#ran","syntology_url":"https://syntology.ai/paper/2006.04388","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04388"}}}},{"paper":"/paper/detectors-detecting-objects-with-recursive-1","slug":"detectors-detecting-objects-with-recursive-1","title":"DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution","date":"2020-06-03","arxiv_id":"2006.02334","rows_on_this_dataset":6,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["joe-siyuan-qiao/DetectoRS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/detectors-detecting-objects-with-recursive-1#ran","syntology_url":"https://syntology.ai/paper/2006.02334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.02334"}}}},{"paper":"/paper/end-to-end-object-detection-with-transformers","slug":"end-to-end-object-detection-with-transformers","title":"End-to-End Object Detection with Transformers","date":"2020-05-26","arxiv_id":"2005.12872","rows_on_this_dataset":5,"code_links":37,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":92,"samples_ran":70,"samples_constructed":45,"samples_ran_checked":62,"samples_ran_instrument_failed":8,"samples_unverified":22,"pointer_only_for_licence":19,"official":{"repos":["facebookresearch/detr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/end-to-end-object-detection-with-transformers#ran","syntology_url":"https://syntology.ai/paper/2005.12872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.12872"}}}},{"paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","slug":"yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","arxiv_id":"2004.10934","rows_on_this_dataset":4,"code_links":223,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":184,"samples_ran":142,"samples_constructed":0,"samples_ran_checked":133,"samples_ran_instrument_failed":9,"samples_unverified":42,"pointer_only_for_licence":21,"official":{"repos":["AlexeyAB/darknet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/yolov4-optimal-speed-and-accuracy-of-object#ran","syntology_url":"https://syntology.ai/paper/2004.10934","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.10934"}}}},{"paper":"/paper/resnest-split-attention-networks","slug":"resnest-split-attention-networks","title":"ResNeSt: Split-Attention Networks","date":"2020-04-19","arxiv_id":"2004.08955","rows_on_this_dataset":11,"code_links":36,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":48,"samples_ran":28,"samples_constructed":0,"samples_ran_checked":25,"samples_ran_instrument_failed":3,"samples_unverified":20,"pointer_only_for_licence":23,"official":{"repos":["zhanghang1989/ResNeSt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/resnest-split-attention-networks#ran","syntology_url":"https://syntology.ai/paper/2004.08955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.08955"}}}},{"paper":"/paper/dynamic-r-cnn-towards-high-quality-object","slug":"dynamic-r-cnn-towards-high-quality-object","title":"Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training","date":"2020-04-13","arxiv_id":"2004.06002","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":18,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":9,"samples_ran_instrument_failed":0,"samples_unverified":9,"pointer_only_for_licence":1,"official":{"repos":["hkzhang95/DynamicRCNN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/dynamic-r-cnn-towards-high-quality-object#ran","syntology_url":"https://syntology.ai/paper/2004.06002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.06002"}}}},{"paper":"/paper/oscar-object-semantics-aligned-pre-training","slug":"oscar-object-semantics-aligned-pre-training","title":"Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks","date":"2020-04-13","arxiv_id":"2004.06165","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":23,"samples_ran":13,"samples_constructed":6,"samples_ran_checked":10,"samples_ran_instrument_failed":3,"samples_unverified":10,"pointer_only_for_licence":1,"official":{"repos":["microsoft/Oscar"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/oscar-object-semantics-aligned-pre-training#ran","syntology_url":"https://syntology.ai/paper/2004.06165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.06165"}}}},{"paper":"/paper/instance-aware-context-focused-and-memory","slug":"instance-aware-context-focused-and-memory","title":"Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection","date":"2020-04-09","arxiv_id":"2004.04725","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["NVlabs/wetectron"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/instance-aware-context-focused-and-memory#ran","syntology_url":"https://syntology.ai/paper/2004.04725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.04725"}}}},{"paper":"/paper/solov2-dynamic-faster-and-stronger","slug":"solov2-dynamic-faster-and-stronger","title":"SOLOv2: Dynamic and Fast Instance Segmentation","date":"2020-03-23","arxiv_id":"2003.10152","rows_on_this_dataset":1,"code_links":18,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":38,"samples_ran":23,"samples_constructed":4,"samples_ran_checked":15,"samples_ran_instrument_failed":8,"samples_unverified":15,"pointer_only_for_licence":24,"official":{"repos":["WXinlong/SOLO"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/solov2-dynamic-faster-and-stronger#ran","syntology_url":"https://syntology.ai/paper/2003.10152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.10152"}}}},{"paper":"/paper/axial-deeplab-stand-alone-axial-attention-for","slug":"axial-deeplab-stand-alone-axial-attention-for","title":"Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation","date":"2020-03-17","arxiv_id":"2003.07853","rows_on_this_dataset":5,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":1,"official":{"repos":["google-research/deeplab2"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["named_in_paper"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/axial-deeplab-stand-alone-axial-attention-for#ran","syntology_url":"https://syntology.ai/paper/2003.07853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.07853"}}}},{"paper":"/paper/frustratingly-simple-few-shot-object","slug":"frustratingly-simple-few-shot-object","title":"Frustratingly Simple Few-Shot Object Detection","date":"2020-03-16","arxiv_id":"2003.06957","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":6,"pointer_only_for_licence":11,"official":{"repos":["ucbdrive/few-shot-object-detection"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/frustratingly-simple-few-shot-object#ran","syntology_url":"https://syntology.ai/paper/2003.06957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.06957"}}}},{"paper":"/paper/learning-delicate-local-representations-for","slug":"learning-delicate-local-representations-for","title":"Learning Delicate Local Representations for Multi-Person Pose Estimation","date":"2020-03-09","arxiv_id":"2003.04030","rows_on_this_dataset":5,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["caiyuanhao1998/RSN"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-delicate-local-representations-for#ran","syntology_url":"https://syntology.ai/paper/2003.04030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.04030"}}}},{"paper":"/paper/imram-iterative-matching-with-recurrent","slug":"imram-iterative-matching-with-recurrent","title":"IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval","date":"2020-03-08","arxiv_id":"2003.03772","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":3,"official":{"repos":["HuiChen24/IMRAM"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/imram-iterative-matching-with-recurrent#ran","syntology_url":"https://syntology.ai/paper/2003.03772","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.03772"}}}},{"paper":"/paper/cross-iteration-batch-normalization","slug":"cross-iteration-batch-normalization","title":"Cross-Iteration Batch Normalization","date":"2020-02-13","arxiv_id":"2002.05712","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":1,"official":{"repos":["Howal/Cross-iterationBatchNorm"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/cross-iteration-batch-normalization#ran","syntology_url":"https://syntology.ai/paper/2002.05712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05712"}}}},{"paper":"/paper/m2-meshed-memory-transformer-for-image","slug":"m2-meshed-memory-transformer-for-image","title":"Meshed-Memory Transformer for Image Captioning","date":"2019-12-17","arxiv_id":"1912.08226","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":3,"official":{"repos":["aimagelab/meshed-memory-transformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/m2-meshed-memory-transformer-for-image#ran","syntology_url":"https://syntology.ai/paper/1912.08226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.08226"}}}},{"paper":"/paper/learning-canonical-representations-for-scene","slug":"learning-canonical-representations-for-scene","title":"Learning Canonical Representations for Scene Graph to Image Generation","date":"2019-12-16","arxiv_id":"1912.07414","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":17,"samples_ran":10,"samples_constructed":0,"samples_ran_checked":8,"samples_ran_instrument_failed":2,"samples_unverified":7,"pointer_only_for_licence":0,"official":{"repos":["roeiherz/CanonicalSg2Im"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":7,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-canonical-representations-for-scene#ran","syntology_url":"https://syntology.ai/paper/1912.07414","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.07414"}}}},{"paper":"/paper/bridging-the-gap-between-anchor-based-and","slug":"bridging-the-gap-between-anchor-based-and","title":"Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection","date":"2019-12-05","arxiv_id":"1912.02424","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["sfzhang15/ATSS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/bridging-the-gap-between-anchor-based-and#ran","syntology_url":"https://syntology.ai/paper/1912.02424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.02424"}}}},{"paper":"/paper/multiple-anchor-learning-for-visual-object","slug":"multiple-anchor-learning-for-visual-object","title":"Multiple Anchor Learning for Visual Object Detection","date":"2019-12-04","arxiv_id":"1912.02252","rows_on_this_dataset":3,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/multiple-anchor-learning-for-visual-object#ran","syntology_url":"https://syntology.ai/paper/1912.02252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.02252"}}}},{"paper":"/paper/one-shot-object-detection-with-co-attention-1","slug":"one-shot-object-detection-with-co-attention-1","title":"One-Shot Object Detection with Co-Attention and Co-Excitation","date":"2019-11-28","arxiv_id":"1911.12529","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["timy90022/One-Shot-Object-Detection"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/one-shot-object-detection-with-co-attention-1#ran","syntology_url":"https://syntology.ai/paper/1911.12529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.12529"}}}},{"paper":"/paper/soft-anchor-point-object-detection","slug":"soft-anchor-point-object-detection","title":"Soft Anchor-Point Object Detection","date":"2019-11-27","arxiv_id":"1911.12448","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/soft-anchor-point-object-detection#ran","syntology_url":"https://syntology.ai/paper/1911.12448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.12448"}}}},{"paper":"/paper/panoptic-deeplab-a-simple-strong-and-fast","slug":"panoptic-deeplab-a-simple-strong-and-fast","title":"Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation","date":"2019-11-22","arxiv_id":"1911.10194","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":1,"samples_unverified":4,"pointer_only_for_licence":1,"official":{"repos":["bowenc0221/panoptic-deeplab"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/panoptic-deeplab-a-simple-strong-and-fast#ran","syntology_url":"https://syntology.ai/paper/1911.10194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.10194"}}}},{"paper":"/paper/multi-label-classification-with-label-graph","slug":"multi-label-classification-with-label-graph","title":"Multi-Label Classification with Label Graph Superimposing","date":"2019-11-21","arxiv_id":"1911.09243","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":2,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/multi-label-classification-with-label-graph#ran","syntology_url":"https://syntology.ai/paper/1911.09243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.09243"}}}},{"paper":"/paper/efficientdet-scalable-and-efficient-object","slug":"efficientdet-scalable-and-efficient-object","title":"EfficientDet: Scalable and Efficient Object Detection","date":"2019-11-20","arxiv_id":"1911.09070","rows_on_this_dataset":3,"code_links":64,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":70,"samples_ran":55,"samples_constructed":1,"samples_ran_checked":48,"samples_ran_instrument_failed":7,"samples_unverified":15,"pointer_only_for_licence":7,"official":{"repos":["google/automl"],"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":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/efficientdet-scalable-and-efficient-object#ran","syntology_url":"https://syntology.ai/paper/1911.09070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.09070"}}}},{"paper":"/paper/centermask-real-time-anchor-free-instance-1","slug":"centermask-real-time-anchor-free-instance-1","title":"CenterMask : Real-Time Anchor-Free Instance Segmentation","date":"2019-11-15","arxiv_id":"1911.06667","rows_on_this_dataset":16,"code_links":8,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["youngwanLEE/CenterMask"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/centermask-real-time-anchor-free-instance-1#ran","syntology_url":"https://syntology.ai/paper/1911.06667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06667"}}}},{"paper":"/paper/191013321","slug":"191013321","title":"Semantic Object Accuracy for Generative Text-to-Image Synthesis","date":"2019-10-29","arxiv_id":"1910.13321","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":23,"samples_ran":18,"samples_constructed":0,"samples_ran_checked":14,"samples_ran_instrument_failed":4,"samples_unverified":5,"pointer_only_for_licence":1,"official":{"repos":["tohinz/semantic-object-accuracy-for-generative-text-to-image-synthesis"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/191013321#ran","syntology_url":"https://syntology.ai/paper/1910.13321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13321"}}}},{"paper":"/paper/distribution-aware-coordinate-representation","slug":"distribution-aware-coordinate-representation","title":"Distribution-Aware Coordinate Representation for Human Pose Estimation","date":"2019-10-14","arxiv_id":"1910.06278","rows_on_this_dataset":3,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":5,"samples_unverified":3,"pointer_only_for_licence":1,"official":{"repos":["leoxiaobin/deep-high-resolution-net.pytorch"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/distribution-aware-coordinate-representation#ran","syntology_url":"https://syntology.ai/paper/1910.06278","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.06278"}}}},{"paper":"/paper/polarmask-single-shot-instance-segmentation","slug":"polarmask-single-shot-instance-segmentation","title":"PolarMask: Single Shot Instance Segmentation with Polar Representation","date":"2019-09-29","arxiv_id":"1909.13226","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["xieenze/PolarMask"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/polarmask-single-shot-instance-segmentation#ran","syntology_url":"https://syntology.ai/paper/1909.13226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.13226"}}}},{"paper":"/paper/cbnet-a-novel-composite-backbone-network","slug":"cbnet-a-novel-composite-backbone-network","title":"CBNet: A Novel Composite Backbone Network Architecture for Object Detection","date":"2019-09-09","arxiv_id":"1909.03625","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":1,"official":{"repos":["PKUbahuangliuhe/CBNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/cbnet-a-novel-composite-backbone-network#ran","syntology_url":"https://syntology.ai/paper/1909.03625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.03625"}}}},{"paper":"/paper/visual-semantic-reasoning-for-image-text","slug":"visual-semantic-reasoning-for-image-text","title":"Visual Semantic Reasoning for Image-Text Matching","date":"2019-09-06","arxiv_id":"1909.02701","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["KunpengLi1994/VSRN"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/visual-semantic-reasoning-for-image-text#ran","syntology_url":"https://syntology.ai/paper/1909.02701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.02701"}}}},{"paper":"/paper/freeanchor-learning-to-match-anchors-for","slug":"freeanchor-learning-to-match-anchors-for","title":"FreeAnchor: Learning to Match Anchors for Visual Object Detection","date":"2019-09-05","arxiv_id":"1909.02466","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["zhangxiaosong18/FreeAnchor"],"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/freeanchor-learning-to-match-anchors-for#ran","syntology_url":"https://syntology.ai/paper/1909.02466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.02466"}}}}],"record_sha256":"980929071d27d03f6ce4d12701bdbfeb443eb49bc500dbb0d05067b1a334e9be","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}