{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/clothes-changing-person-re-identification","title":"Clothes-Changing Person Re-identification with RGB Modality Only","arxiv_id":"2204.06890","date":"2022-04-14","proceeding":"CVPR 2022 1","authors":["Xinqian Gu","Hong Chang","Bingpeng Ma","Shutao Bai","Shiguang Shan","Xilin Chen"],"abstract":"The key to address clothes-changing person re-identification (re-id) is to extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and gait. Most current works mainly focus on modeling body shape from multi-modality information (e.g., silhouettes and sketches), but do not make full use of the clothes-irrelevant information in the original RGB images. In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w.r.t. clothes. Extensive experiments demonstrate that using RGB images only, CAL outperforms all state-of-the-art methods on widely-used clothes-changing person re-id benchmarks. Besides, compared with images, videos contain richer appearance and additional temporal information, which can be used to model proper spatiotemporal patterns to assist clothes-changing re-id. Since there is no publicly available clothes-changing video re-id dataset, we contribute a new dataset named CCVID and show that there exists much room for improvement in modeling spatiotemporal information. The code and new dataset are available at: https://github.com/guxinqian/Simple-CCReID.","url_abs":"https://arxiv.org/abs/2204.06890v1","url_pdf":"https://arxiv.org/pdf/2204.06890v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"clothes-changing-person-re-identification","repo_url":"https://github.com/guxinqian/simple-ccreid","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clothes-changing-person-re-identification","task_name":"Clothes Changing Person Re-Identification"},{"task_slug":"multiview-gait-recognition","task_name":"Multiview Gait Recognition"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"}],"methods":[],"datasets_introduced":[{"slug":"ccvid","name":"CCVID","full_name":"Clothes-Changing Video person re-ID"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/multiview-gait-recognition-on-casia-b","task":"Multiview Gait Recognition","dataset":"CASIA-B","model":"CAL (RGB), AP3DNLResNet50","rank_in_archive_order":1,"of":12,"metrics":{"Accuracy (Cross-View, Avg)":"97.3","BG#1-2":"99.8","CL#1-2":"92.3","NM#5-6":"99.9"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-ccvid","task":"Person Re-Identification","dataset":"CCVID","model":"CAL","rank_in_archive_order":4,"of":4,"metrics":{"Rank-1":"81.7","mAP":"79.6"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-ltcc","task":"Person Re-Identification","dataset":"LTCC","model":"CAL","rank_in_archive_order":12,"of":13,"metrics":{"Rank-1":"40.1","mAP":"18.0"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-prcc","task":"Person Re-Identification","dataset":"PRCC","model":"CAL","rank_in_archive_order":10,"of":13,"metrics":{" Rank-1":"55.2","mAP":"55.8"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-vc-clothes","task":"Person Re-Identification","dataset":"VC-Clothes","model":"CAL","rank_in_archive_order":2,"of":6,"metrics":{" Rank-1":"85.8","mAP":"79.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.06890","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.06890"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/guxinqian/Simple-CCReID","reach":null}],"summary":{"ran":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"3a6789ecfe9c5c13","entry":"ClothesBasedAdversarialLoss","repo":"guxinqian/Simple-CCReID","repo_kind":"official","path":"losses/clothes_based_adversarial_loss.py","file_url":"https://github.com/guxinqian/Simple-CCReID/blob/HEAD/losses/clothes_based_adversarial_loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"3a6789ecfe9c5c13"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}