{"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/davis/papers/ran/1","list_of":"/dataset/davis","dataset":"DAVIS","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":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,25],"of":25,"counts":{"papers_with_a_benchmark_row":58,"with_a_code_link":52,"where_syntology_ran_a_sample":25,"not_listed_spam_title":0,"listed":58,"listed_where_code_ran":25,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":17,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":17,"listed_every_run_a_failure_of_syntologys_instrument":8,"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/davis/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/cfr-icl-cascade-forward-refinement-with","slug":"cfr-icl-cascade-forward-refinement-with","title":"CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation","date":"2023-03-09","arxiv_id":"2303.05620","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":1,"official":{"repos":["TitorX/CFR-ICL-Interactive-Segmentation"],"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/cfr-icl-cascade-forward-refinement-with#ran","syntology_url":"https://syntology.ai/paper/2303.05620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.05620"}}}},{"paper":"/paper/simpleclick-interactive-image-segmentation","slug":"simpleclick-interactive-image-segmentation","title":"SimpleClick: Interactive Image Segmentation with Simple Vision Transformers","date":"2022-10-20","arxiv_id":"2210.11006","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["uncbiag/simpleclick"],"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","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/simpleclick-interactive-image-segmentation#ran","syntology_url":"https://syntology.ai/paper/2210.11006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11006"}}}},{"paper":"/paper/real-time-streaming-video-denoising-with","slug":"real-time-streaming-video-denoising-with","title":"Real-time Streaming Video Denoising with Bidirectional Buffers","date":"2022-07-14","arxiv_id":"2207.06937","rows_on_this_dataset":2,"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":0,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["chenyangqiqi/bsvd"],"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/real-time-streaming-video-denoising-with#ran","syntology_url":"https://syntology.ai/paper/2207.06937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06937"}}}},{"paper":"/paper/tackling-background-distraction-in-video","slug":"tackling-background-distraction-in-video","title":"Tackling Background Distraction in Video Object Segmentation","date":"2022-07-14","arxiv_id":"2207.06953","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["suhwan-cho/tbd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/tackling-background-distraction-in-video#ran","syntology_url":"https://syntology.ai/paper/2207.06953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06953"}}}},{"paper":"/paper/xmem-long-term-video-object-segmentation-with","slug":"xmem-long-term-video-object-segmentation-with","title":"XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model","date":"2022-07-14","arxiv_id":"2207.07115","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":2,"official":{"repos":["hkchengrex/XMem"],"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/xmem-long-term-video-object-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/2207.07115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07115"}}}},{"paper":"/paper/recurrent-video-restoration-transformer-with","slug":"recurrent-video-restoration-transformer-with","title":"Recurrent Video Restoration Transformer with Guided Deformable Attention","date":"2022-06-05","arxiv_id":"2206.02146","rows_on_this_dataset":5,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":14,"samples_ran":12,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":7,"samples_unverified":2,"pointer_only_for_licence":7,"official":{"repos":["jingyunliang/rvrt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/recurrent-video-restoration-transformer-with#ran","syntology_url":"https://syntology.ai/paper/2206.02146","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.02146"}}}},{"paper":"/paper/focalclick-towards-practical-interactive","slug":"focalclick-towards-practical-interactive","title":"FocalClick: Towards Practical Interactive Image Segmentation","date":"2022-04-06","arxiv_id":"2204.02574","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":21,"samples_ran":14,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":4,"samples_unverified":7,"pointer_only_for_licence":1,"official":{"repos":["XavierCHEN34/ClickSEG"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/focalclick-towards-practical-interactive#ran","syntology_url":"https://syntology.ai/paper/2204.02574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02574"}}}},{"paper":"/paper/towards-an-end-to-end-framework-for-flow","slug":"towards-an-end-to-end-framework-for-flow","title":"Towards An End-to-End Framework for Flow-Guided Video Inpainting","date":"2022-04-06","arxiv_id":"2204.02663","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":5,"samples_constructed":2,"samples_ran_checked":2,"samples_ran_instrument_failed":3,"samples_unverified":1,"pointer_only_for_licence":6,"official":{"repos":["MCG-NKU/E2FGVI"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"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/towards-an-end-to-end-framework-for-flow#ran","syntology_url":"https://syntology.ai/paper/2204.02663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02663"}}}},{"paper":"/paper/vrt-a-video-restoration-transformer","slug":"vrt-a-video-restoration-transformer","title":"VRT: A Video Restoration Transformer","date":"2022-01-28","arxiv_id":"2201.12288","rows_on_this_dataset":5,"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":0,"samples_ran_instrument_failed":4,"samples_unverified":1,"pointer_only_for_licence":5,"official":{"repos":["jingyunliang/vrt"],"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","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/vrt-a-video-restoration-transformer#ran","syntology_url":"https://syntology.ai/paper/2201.12288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12288"}}}},{"paper":"/paper/hierarchical-memory-matching-network-for","slug":"hierarchical-memory-matching-network-for","title":"Hierarchical Memory Matching Network for Video Object Segmentation","date":"2021-09-23","arxiv_id":"2109.11404","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":5,"samples_constructed":4,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":8,"official":{"repos":["hongje/hmmn"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/hierarchical-memory-matching-network-for#ran","syntology_url":"https://syntology.ai/paper/2109.11404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.11404"}}}},{"paper":"/paper/edgeflow-achieving-practical-interactive","slug":"edgeflow-achieving-practical-interactive","title":"EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow","date":"2021-09-20","arxiv_id":"2109.09406","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["PaddlePaddle/PaddleSeg"],"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/edgeflow-achieving-practical-interactive#ran","syntology_url":"https://syntology.ai/paper/2109.09406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.09406"}}}},{"paper":"/paper/fuseformer-fusing-fine-grained-information-in","slug":"fuseformer-fusing-fine-grained-information-in","title":"FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting","date":"2021-09-07","arxiv_id":"2109.02974","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":10,"samples_constructed":8,"samples_ran_checked":10,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":11,"official":{"repos":["ruiliu-ai/fuseformer"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":8,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/fuseformer-fusing-fine-grained-information-in#ran","syntology_url":"https://syntology.ai/paper/2109.02974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.02974"}}}},{"paper":"/paper/patch-craft-video-denoising-by-deep-modeling","slug":"patch-craft-video-denoising-by-deep-modeling","title":"Patch Craft: Video Denoising by Deep Modeling and Patch Matching","date":"2021-03-25","arxiv_id":"2103.13767","rows_on_this_dataset":5,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":4,"samples_constructed":4,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":8,"official":{"repos":["grishavak/PaCNet-denoiser"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/patch-craft-video-denoising-by-deep-modeling#ran","syntology_url":"https://syntology.ai/paper/2103.13767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13767"}}}},{"paper":"/paper/efficient-regional-memory-network-for-video","slug":"efficient-regional-memory-network-for-video","title":"Efficient Regional Memory Network for Video Object Segmentation","date":"2021-03-24","arxiv_id":"2103.12934","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":5,"pointer_only_for_licence":0,"official":{"repos":["hzxie/RMNet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/efficient-regional-memory-network-for-video#ran","syntology_url":"https://syntology.ai/paper/2103.12934","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12934"}}}},{"paper":"/paper/reviving-iterative-training-with-mask","slug":"reviving-iterative-training-with-mask","title":"Reviving Iterative Training with Mask Guidance for Interactive Segmentation","date":"2021-02-12","arxiv_id":"2102.06583","rows_on_this_dataset":2,"code_links":5,"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":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/reviving-iterative-training-with-mask#ran","syntology_url":"https://syntology.ai/paper/2102.06583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.06583"}}}},{"paper":"/paper/sstvos-sparse-spatiotemporal-transformers-for","slug":"sstvos-sparse-spatiotemporal-transformers-for","title":"SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation","date":"2021-01-21","arxiv_id":"2101.08833","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":5,"samples_constructed":5,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":7,"official":{"repos":["dukebw/SSTVOS"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/sstvos-sparse-spatiotemporal-transformers-for#ran","syntology_url":"https://syntology.ai/paper/2101.08833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.08833"}}}},{"paper":"/paper/learning-joint-spatial-temporal","slug":"learning-joint-spatial-temporal","title":"Learning Joint Spatial-Temporal Transformations for Video Inpainting","date":"2020-07-20","arxiv_id":"2007.10247","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":1,"samples_unverified":4,"pointer_only_for_licence":0,"official":{"repos":["researchmm/STTN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-joint-spatial-temporal#ran","syntology_url":"https://syntology.ai/paper/2007.10247","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10247"}}}},{"paper":"/paper/ranet-ranking-attention-network-for-fast","slug":"ranet-ranking-attention-network-for-fast","title":"RANet: Ranking Attention Network for Fast Video Object Segmentation","date":"2019-08-19","arxiv_id":"1908.06647","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":11,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["Storife/RANet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ranet-ranking-attention-network-for-fast#ran","syntology_url":"https://syntology.ai/paper/1908.06647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.06647"}}}},{"paper":"/paper/fastdvdnet-towards-real-time-video-denoising","slug":"fastdvdnet-towards-real-time-video-denoising","title":"FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation","date":"2019-07-01","arxiv_id":"1907.01361","rows_on_this_dataset":5,"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":0,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["m-tassano/fastdvdnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/fastdvdnet-towards-real-time-video-denoising#ran","syntology_url":"https://syntology.ai/paper/1907.01361","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.01361"}}}},{"paper":"/paper/deep-flow-guided-video-inpainting","slug":"deep-flow-guided-video-inpainting","title":"Deep Flow-Guided Video Inpainting","date":"2019-05-08","arxiv_id":"1905.02884","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["nbei/Deep-Flow-Guided-Video-Inpainting"],"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/deep-flow-guided-video-inpainting#ran","syntology_url":"https://syntology.ai/paper/1905.02884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.02884"}}}},{"paper":"/paper/deep-video-inpainting","slug":"deep-video-inpainting","title":"Deep Video Inpainting","date":"2019-05-05","arxiv_id":"1905.01639","rows_on_this_dataset":1,"code_links":2,"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":{"repos":["mcahny/Deep-Video-Inpainting"],"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/deep-video-inpainting#ran","syntology_url":"https://syntology.ai/paper/1905.01639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.01639"}}}},{"paper":"/paper/spatiotemporal-cnn-for-video-object","slug":"spatiotemporal-cnn-for-video-object","title":"Spatiotemporal CNN for Video Object Segmentation","date":"2019-04-04","arxiv_id":"1904.02363","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":1,"official":{"repos":["longyin880815/STCNN"],"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/spatiotemporal-cnn-for-video-object#ran","syntology_url":"https://syntology.ai/paper/1904.02363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02363"}}}},{"paper":"/paper/video-object-segmentation-using-space-time","slug":"video-object-segmentation-using-space-time","title":"Video Object Segmentation using Space-Time Memory Networks","date":"2019-04-01","arxiv_id":"1904.00607","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":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/video-object-segmentation-using-space-time#ran","syntology_url":"https://syntology.ai/paper/1904.00607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00607"}}}},{"paper":"/paper/feelvos-fast-end-to-end-embedding-learning","slug":"feelvos-fast-end-to-end-embedding-learning","title":"FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation","date":"2019-02-25","arxiv_id":"1902.09513","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":8,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["tensorflow/models"],"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/feelvos-fast-end-to-end-embedding-learning#ran","syntology_url":"https://syntology.ai/paper/1902.09513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09513"}}}},{"paper":"/paper/deep-interactive-object-selection","slug":"deep-interactive-object-selection","title":"Deep Interactive Object Selection","date":"2016-03-13","arxiv_id":"1603.04042","rows_on_this_dataset":2,"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":3,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/deep-interactive-object-selection#ran","syntology_url":"https://syntology.ai/paper/1603.04042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.04042"}}}}],"record_sha256":"e98aa756c96ff517755485da14e470d2450d1539dbdfd77c5d6148c409d5e294","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}