{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/video-based-person-re-identification/papers/2","list_of":"/task/video-based-person-re-identification","task":"Video-Based Person Re-Identification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,117],"of":117,"counts":{"archive_papers_tagged":117,"with_a_code_link":38,"where_syntology_ran_a_sample":3,"not_listed_spam_title":0,"listed":117,"listed_where_code_ran":3,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":3,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/video-based-person-re-identification","prev":"/task/video-based-person-re-identification","next":null,"papers":[{"url":null,"slug":"where-and-when-to-look-deep-siamese-attention","title":"Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification","date":"2018-08-03","arxiv_id":"1808.01911","repositories_listed":0,"syntology":null},{"url":null,"slug":"scan-self-and-collaborative-attention-network","title":"SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification","date":"2018-07-16","arxiv_id":"1807.05688","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-synergic-residual-learning","title":"Spatial-Temporal Synergic Residual Learning for Video Person Re-Identification","date":"2018-07-16","arxiv_id":"1807.05799","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-based-person-re-identification-via-3d","title":"Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention","date":"2018-07-12","arxiv_id":"1807.05073","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-spatial-and-temporal-features-mixture-model","title":"A Spatial and Temporal Features Mixture Model with Body Parts for Video-based Person Re-Identification","date":"2018-07-03","arxiv_id":"1807.00975","repositories_listed":0,"syntology":null},{"url":"/paper/exploit-the-unknown-gradually-one-shot-video","slug":"exploit-the-unknown-gradually-one-shot-video","title":"Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/video-person-re-identification-with","slug":"video-person-re-identification-with","title":"Video Person Re-Identification With Competitive Snippet-Similarity Aggregation and Co-Attentive Snippet Embedding","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"diversity-regularized-spatiotemporal","title":"Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification","date":"2018-03-27","arxiv_id":"1803.09882","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-person-re-identification-by-temporal","title":"Video Person Re-identification by Temporal Residual Learning","date":"2018-02-22","arxiv_id":"1802.07918","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-stream-convolutional-networks-for-video","title":"Three-Stream Convolutional Networks for Video-based Person Re-Identification","date":"2017-11-22","arxiv_id":"1712.01652","repositories_listed":0,"syntology":null},{"url":"/paper/stepwise-metric-promotion-for-unsupervised","slug":"stepwise-metric-promotion-for-unsupervised","title":"Stepwise Metric Promotion for Unsupervised Video Person Re-Identification","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"see-the-forest-for-the-trees-joint-spatial","title":"See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-Identification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-compact-appearance-representation","title":"Learning Compact Appearance Representation for Video-based Person Re-Identification","date":"2017-02-21","arxiv_id":"1702.06294","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-based-person-re-identification-with","title":"Video-based Person Re-identification with Accumulative Motion Context","date":"2017-01-01","arxiv_id":"1701.00193","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-recurrent-convolutional-networks-for","title":"Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach","date":"2016-06-06","arxiv_id":"1606.01609","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-convolutional-network-for-video","title":"Recurrent Convolutional Network for Video-Based Person Re-Identification","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"top-push-video-based-person-re-identification","title":"Top-push Video-based Person Re-identification","date":"2016-04-29","arxiv_id":"1604.08683","repositories_listed":0,"syntology":null}],"record_sha256":"40b249b7ed2a572dbbedb0f1e68df28a22690c56295fa0898d4a631ea8fa908e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}