{"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/hierarchical-spatio-temporal-representation","title":"Hierarchical Spatio-Temporal Representation Learning for Gait Recognition","arxiv_id":"2307.09856","date":"2023-07-19","proceeding":"ICCV 2023 1","authors":["Lei Wang","Bo Liu","Fangfang Liang","Bincheng Wang"],"abstract":"Gait recognition is a biometric technique that identifies individuals by their unique walking styles, which is suitable for unconstrained environments and has a wide range of applications. While current methods focus on exploiting body part-based representations, they often neglect the hierarchical dependencies between local motion patterns. In this paper, we propose a hierarchical spatio-temporal representation learning (HSTL) framework for extracting gait features from coarse to fine. Our framework starts with a hierarchical clustering analysis to recover multi-level body structures from the whole body to local details. Next, an adaptive region-based motion extractor (ARME) is designed to learn region-independent motion features. The proposed HSTL then stacks multiple ARMEs in a top-down manner, with each ARME corresponding to a specific partition level of the hierarchy. An adaptive spatio-temporal pooling (ASTP) module is used to capture gait features at different levels of detail to perform hierarchical feature mapping. Finally, a frame-level temporal aggregation (FTA) module is employed to reduce redundant information in gait sequences through multi-scale temporal downsampling. Extensive experiments on CASIA-B, OUMVLP, GREW, and Gait3D datasets demonstrate that our method outperforms the state-of-the-art while maintaining a reasonable balance between model accuracy and complexity.","url_abs":"https://arxiv.org/abs/2307.09856v1","url_pdf":"https://arxiv.org/pdf/2307.09856v1.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":"hierarchical-spatio-temporal-representation","repo_url":"https://github.com/gudaochangsheng/HSTL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"gait-recognition","task_name":"Gait Recognition"},{"task_slug":"gait-recognition-in-the-wild","task_name":"Gait Recognition in the Wild"},{"task_slug":"multiview-gait-recognition","task_name":"Multiview Gait Recognition"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/gait-recognition-on-gait3d","task":"Gait Recognition","dataset":"Gait3D","model":"HSTL","rank_in_archive_order":1,"of":2,"metrics":{"Rank-1":"61.30","Rank-5":"76.3","mAP":"55.48","mINP":"34.77"},"uses_additional_data":false},{"leaderboard":"/sota/gait-recognition-on-oumvlp","task":"Gait Recognition","dataset":"OUMVLP","model":"HSTL","rank_in_archive_order":2,"of":7,"metrics":{"Averaged rank-1 acc(%)":"92.4"},"uses_additional_data":false},{"leaderboard":"/sota/gait-recognition-in-the-wild-on-gait3d","task":"Gait Recognition in the Wild","dataset":"Gait3D","model":"HSTL","rank_in_archive_order":1,"of":2,"metrics":{"Rank-1":"61.3"},"uses_additional_data":false},{"leaderboard":"/sota/multiview-gait-recognition-on-casia-b","task":"Multiview Gait Recognition","dataset":"CASIA-B","model":"HSTL","rank_in_archive_order":4,"of":12,"metrics":{"Accuracy (Cross-View, Avg)":"94.3","BG#1-2":"95.9","CL#1-2":"88.9","NM#5-6 ":"98.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2307.09856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09856"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/gudaochangsheng/HSTL","reach":{"status":"ok"}}],"summary":{"ran":3,"unverified":2},"by_repo_kind":{"official":{"samples":5,"ran":3,"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":5,"samples":[{"code_sha256_prefix":"259090a2dcfc7c36","entry":"Ntuple","repo":"gudaochangsheng/HSTL","repo_kind":"official","path":"lib/utils/common.py","file_url":"https://github.com/gudaochangsheng/HSTL/blob/HEAD/lib/utils/common.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"259090a2dcfc7c36"}},{"code_sha256_prefix":"b57dd7785b289b3e","entry":"de_diag","repo":"gudaochangsheng/HSTL","repo_kind":"official","path":"lib/utils/evaluation.py","file_url":"https://github.com/gudaochangsheng/HSTL/blob/HEAD/lib/utils/evaluation.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b57dd7785b289b3e"}},{"code_sha256_prefix":"ed7584e83b710d3c","entry":"get_attr_from","repo":"gudaochangsheng/HSTL","repo_kind":"official","path":"lib/utils/common.py","file_url":"https://github.com/gudaochangsheng/HSTL/blob/HEAD/lib/utils/common.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ed7584e83b710d3c"}},{"code_sha256_prefix":"034c16076776159f","entry":"cuda_dist","repo":"gudaochangsheng/HSTL","repo_kind":"official","path":"lib/utils/evaluation.py","file_url":"https://github.com/gudaochangsheng/HSTL/blob/HEAD/lib/utils/evaluation.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"034c16076776159f"}},{"code_sha256_prefix":"bb3d34be2732f020","entry":"get_valid_args","repo":"gudaochangsheng/HSTL","repo_kind":"official","path":"lib/utils/common.py","file_url":"https://github.com/gudaochangsheng/HSTL/blob/HEAD/lib/utils/common.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bb3d34be2732f020"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}