{"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/grouped-spatial-temporal-aggregation-for","title":"Grouped Spatial-Temporal Aggregation for Efficient Action Recognition","arxiv_id":"1909.13130","date":"2019-09-28","proceeding":"ICCV 2019 10","authors":["Chenxu Luo","Alan Yuille"],"abstract":"Temporal reasoning is an important aspect of video analysis. 3D CNN shows good performance by exploring spatial-temporal features jointly in an unconstrained way, but it also increases the computational cost a lot. Previous works try to reduce the complexity by decoupling the spatial and temporal filters. In this paper, we propose a novel decomposition method that decomposes the feature channels into spatial and temporal groups in parallel. This decomposition can make two groups focus on static and dynamic cues separately. We call this grouped spatial-temporal aggregation (GST). This decomposition is more parameter-efficient and enables us to quantitatively analyze the contributions of spatial and temporal features in different layers. We verify our model on several action recognition tasks that require temporal reasoning and show its effectiveness.","url_abs":"https://arxiv.org/abs/1909.13130v1","url_pdf":"https://arxiv.org/pdf/1909.13130v1.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":"grouped-spatial-temporal-aggregation-for","repo_url":"https://github.com/chenxuluo/GST-video","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1909.13130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.13130"}},"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/chenxuluo/GST-video","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"listed":{"samples":5,"ran":0,"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":"cf910ac82480d5a5","entry":"resnet101","repo":"chenxuluo/GST-video","repo_kind":"listed","path":"GST.py","file_url":"https://github.com/chenxuluo/GST-video/blob/HEAD/GST.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cf910ac82480d5a5"}},{"code_sha256_prefix":"b4ca2701239e633c","entry":"resnet50","repo":"chenxuluo/GST-video","repo_kind":"listed","path":"GST.py","file_url":"https://github.com/chenxuluo/GST-video/blob/HEAD/GST.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b4ca2701239e633c"}},{"code_sha256_prefix":"9d78c6cc09711d49","entry":"return_dataset","repo":"chenxuluo/GST-video","repo_kind":"listed","path":"datasets_video.py","file_url":"https://github.com/chenxuluo/GST-video/blob/HEAD/datasets_video.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9d78c6cc09711d49"}},{"code_sha256_prefix":"ef7d80ceb291c003","entry":"return_somethingv1","repo":"chenxuluo/GST-video","repo_kind":"listed","path":"datasets_video.py","file_url":"https://github.com/chenxuluo/GST-video/blob/HEAD/datasets_video.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ef7d80ceb291c003"}},{"code_sha256_prefix":"d9d848105c68dc31","entry":"return_somethingv2","repo":"chenxuluo/GST-video","repo_kind":"listed","path":"datasets_video.py","file_url":"https://github.com/chenxuluo/GST-video/blob/HEAD/datasets_video.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d9d848105c68dc31"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}