{"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/adafocus-towards-end-to-end-weakly-supervised","title":"Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition","arxiv_id":"2311.17118","date":"2023-11-28","proceeding":null,"authors":["Jiaming Zhou","Hanjun Li","Kun-Yu Lin","Junwei Liang"],"abstract":"Developing end-to-end action recognition models on long videos is fundamental and crucial for long-video action understanding. Due to the unaffordable cost of end-to-end training on the whole long videos, existing works generally train models on short clips trimmed from long videos. However, this ``trimming-then-training'' practice requires action interval annotations for clip-level supervision, i.e., knowing which actions are trimmed into the clips. Unfortunately, collecting such annotations is very expensive and prevents model training at scale. To this end, this work aims to build a weakly supervised end-to-end framework for training recognition models on long videos, with only video-level action category labels. Without knowing the precise temporal locations of actions in long videos, our proposed weakly supervised framework, namely AdaptFocus, estimates where and how likely the actions will occur to adaptively focus on informative action clips for end-to-end training. The effectiveness of the proposed AdaptFocus framework is demonstrated on three long-video datasets. Furthermore, for downstream long-video tasks, our AdaptFocus framework provides a weakly supervised feature extraction pipeline for extracting more robust long-video features, such that the state-of-the-art methods on downstream tasks are significantly advanced. We will release the code and models.","url_abs":"https://arxiv.org/abs/2311.17118v2","url_pdf":"https://arxiv.org/pdf/2311.17118v2.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":[],"tasks":[{"task_slug":"action-classification","task_name":"Action Classification"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-segmentation","task_name":"Action Segmentation"},{"task_slug":"action-understanding","task_name":"Action Understanding"},{"task_slug":"long-video-activity-recognition","task_name":"Long-video Activity Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"},{"task_slug":"temporal-sentence-grounding","task_name":"Temporal Sentence Grounding"},{"task_slug":"weakly-supervised-action-segmentation-action","task_name":"Weakly Supervised Action Segmentation (Action Set))"},{"task_slug":"weakly-supervised-learning","task_name":"Weakly-supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-classification-on-charades","task":"Action Classification","dataset":"Charades","model":"AdaFocus (weak supervision, MViT-B-24, 32x3)","rank_in_archive_order":13,"of":49,"metrics":{"MAP":"47.8"},"uses_additional_data":false},{"leaderboard":"/sota/action-classification-on-charades","task":"Action Classification","dataset":"Charades","model":"AdaFocus (weak supervision, MViT-B-K400-pretrain, 16x4)","rank_in_archive_order":28,"of":49,"metrics":{"MAP":"41.4"},"uses_additional_data":false},{"leaderboard":"/sota/action-classification-on-charades","task":"Action Classification","dataset":"Charades","model":"AdaFocus (weak supervision, X3D-L, 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GHRM)","rank_in_archive_order":4,"of":8,"metrics":{"mAP":"69.6"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-sentence-grounding-on-charades-sta","task":"Temporal Sentence Grounding","dataset":"Charades-STA","model":"AdaFocus (Full, MViT-Charades-Pretrain-feature, MMN model)","rank_in_archive_order":2,"of":13,"metrics":{"R1@0.5":"62.4","R1@0.7":"38.6","R5@0.5":"89.4","R5@0.7":"66.4"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-sentence-grounding-on-charades-sta","task":"Temporal Sentence Grounding","dataset":"Charades-STA","model":"AdaFocus (Full, I3D-Charades-Pretrain-feature, MMN model)","rank_in_archive_order":3,"of":13,"metrics":{"R1@0.5":"56.7","R1@0.7":"35.6","R5@0.5":"87.9","R5@0.7":"65.0"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-sentence-grounding-on-charades-sta","task":"Temporal Sentence Grounding","dataset":"Charades-STA","model":"AdaFocus (Weak, MViT-Charades-Pretrain-feature, CPL 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