{"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/g-tad-sub-graph-localization-for-temporal","title":"G-TAD: Sub-Graph Localization for Temporal Action Detection","arxiv_id":"1911.11462","date":"2019-11-26","proceeding":"CVPR 2020 6","authors":["Mengmeng Xu","Chen Zhao","David S. Rojas","Ali Thabet","Bernard Ghanem"],"abstract":"Temporal action detection is a fundamental yet challenging task in video understanding. Video context is a critical cue to effectively detect actions, but current works mainly focus on temporal context, while neglecting semantic context as well as other important context properties. In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem. Specifically, we formulate video snippets as graph nodes, snippet-snippet correlations as edges, and actions associated with context as target sub-graphs. With graph convolution as the basic operation, we design a GCN block called GCNeXt, which learns the features of each node by aggregating its context and dynamically updates the edges in the graph. To localize each sub-graph, we also design an SGAlign layer to embed each sub-graph into the Euclidean space. Extensive experiments show that G-TAD is capable of finding effective video context without extra supervision and achieves state-of-the-art performance on two detection benchmarks. On ActivityNet-1.3, it obtains an average mAP of 34.09%; on THUMOS14, it reaches 51.6% at IoU@0.5 when combined with a proposal processing method. G-TAD code is publicly available at https://github.com/frostinassiky/gtad.","url_abs":"https://arxiv.org/abs/1911.11462v2","url_pdf":"https://arxiv.org/pdf/1911.11462v2.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":"g-tad-sub-graph-localization-for-temporal","repo_url":"https://github.com/Frostinassiky/gtad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"g-tad-sub-graph-localization-for-temporal","repo_url":"https://github.com/812618101/TAL-Demo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"g-tad-sub-graph-localization-for-temporal","repo_url":"https://github.com/carpedkm/G_TAD_customizing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"g-tad-sub-graph-localization-for-temporal","repo_url":"https://github.com/coolbay/VSGN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"g-tad-sub-graph-localization-for-temporal","repo_url":"https://github.com/handhand123/prsa-net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"g-tad-sub-graph-localization-for-temporal","repo_url":"https://github.com/musicalOffering/sola","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"g-tad-sub-graph-localization-for-temporal","repo_url":"https://github.com/sauradip/fewshotQAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/temporal-action-localization-on-activitynet","task":"Temporal Action Localization","dataset":"ActivityNet-1.3","model":"G-TAD","rank_in_archive_order":28,"of":33,"metrics":{"mAP":"34.09","mAP IOU@0.5":"50.36","mAP IOU@0.75":"34.60","mAP IOU@0.95":"9.02"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-action-localization-on-epic-kitchens","task":"Temporal Action Localization","dataset":"EPIC-KITCHENS-100","model":"G-TAD (verb)","rank_in_archive_order":5,"of":6,"metrics":{"Avg mAP (0.1-0.5)":"9.4","mAP IOU@0.1":"12.1","mAP IOU@0.2":"11.0","mAP IOU@0.3":"9.4","mAP IOU@0.4":"8.1","mAP IOU@0.5":"6.5"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-action-localization-on-fineaction","task":"Temporal Action Localization","dataset":"FineAction","model":"G-TAD (i3d feature)","rank_in_archive_order":8,"of":9,"metrics":{"mAP":"9.06","mAP IOU@0.5":"13.74","mAP IOU@0.75":"8.83","mAP IOU@0.95":"3.06"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-action-localization-on-thumos14","task":"Temporal Action Localization","dataset":"THUMOS’14","model":"G-TAD","rank_in_archive_order":41,"of":42,"metrics":{"mAP IOU@0.5":"40.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.11462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.11462"}},"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. 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