{"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/temporal-pyramid-network-for-action","title":"Temporal Pyramid Network for Action Recognition","arxiv_id":"2004.03548","date":"2020-04-07","proceeding":"CVPR 2020 6","authors":["Ceyuan Yang","Yinghao Xu","Jianping Shi","Bo Dai","Bolei Zhou"],"abstract":"Visual tempo characterizes the dynamics and the temporal scale of an action. Modeling such visual tempos of different actions facilitates their recognition. Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle. In this work we propose a generic Temporal Pyramid Network (TPN) at the feature-level, which can be flexibly integrated into 2D or 3D backbone networks in a plug-and-play manner. Two essential components of TPN, the source of features and the fusion of features, form a feature hierarchy for the backbone so that it can capture action instances at various tempos. TPN also shows consistent improvements over other challenging baselines on several action recognition datasets. Specifically, when equipped with TPN, the 3D ResNet-50 with dense sampling obtains a 2% gain on the validation set of Kinetics-400. A further analysis also reveals that TPN gains most of its improvements on action classes that have large variances in their visual tempos, validating the effectiveness of TPN.","url_abs":"https://arxiv.org/abs/2004.03548v2","url_pdf":"https://arxiv.org/pdf/2004.03548v2.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":"temporal-pyramid-network-for-action","repo_url":"https://github.com/decisionforce/TPN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"temporal-pyramid-network-for-action","repo_url":"https://github.com/Zengxianxian727/TPN_paddle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"temporal-pyramid-network-for-action","repo_url":"https://github.com/open-mmlab/mmaction2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"}],"methods":[{"method_slug":"tpn","method_name":"TPN"}],"datasets_introduced":[],"methods_introduced":[{"slug":"tpn","name":"TPN","full_name":"Temporal Pyramid Network"}],"results":[{"leaderboard":"/sota/action-recognition-in-videos-on-something","task":"Action Recognition","dataset":"Something-Something V2","model":"TPN (TSM-50)","rank_in_archive_order":106,"of":123,"metrics":{"Top-1 Accuracy":"62.0"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.03548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03548"}},"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/Zengxianxian727/TPN_paddle","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/decisionforce/TPN","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/open-mmlab/mmaction2","reach":null}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"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":"380e288d5962b717","entry":"weighted_binary_cross_entropy","repo":"decisionforce/TPN","repo_kind":"official","path":"mmaction/losses/losses.py","file_url":"https://github.com/decisionforce/TPN/blob/HEAD/mmaction/losses/losses.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"380e288d5962b717"}},{"code_sha256_prefix":"8a052a08051eb8bb","entry":"weighted_cross_entropy","repo":"decisionforce/TPN","repo_kind":"official","path":"mmaction/losses/losses.py","file_url":"https://github.com/decisionforce/TPN/blob/HEAD/mmaction/losses/losses.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8a052a08051eb8bb"}},{"code_sha256_prefix":"48433fd136b26537","entry":"weighted_nll_loss","repo":"decisionforce/TPN","repo_kind":"official","path":"mmaction/losses/losses.py","file_url":"https://github.com/decisionforce/TPN/blob/HEAD/mmaction/losses/losses.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"48433fd136b26537"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}