{"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/multi-modal-multi-action-video-recognition","title":"Multi-Modal Multi-Action Video Recognition","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Zhensheng Shi","Ju Liang","Qianqian Li","Haiyong Zheng","Zhaorui Gu","Junyu Dong","Bing Zheng"],"abstract":"    Multi-action video recognition is much more challenging due to the requirement to recognize multiple actions co-occurring simultaneously or sequentially. Modeling multi-action relations is beneficial and crucial to understand videos with multiple actions, and actions in a video are usually presented in multiple modalities. In this paper, we propose a novel multi-action relation model for videos, by leveraging both relational graph convolutional networks (GCNs) and video multi-modality. We first build multi-modal GCNs to explore modality-aware multi-action relations, fed by modality-specific action representation as node features, e.g., spatiotemporal features learned by 3D convolutional neural network (CNN), audio and textual embeddings queried from respective feature lexicons. We then joint both multi-modal CNN-GCN models and multi-modal feature representations for learning better relational action predictions. Ablation study, multi-action relation visualization, and boosts analysis, all show efficacy of our multi-modal multi-action relation modeling. Also our method achieves state-of-the-art performance on large-scale multi-action M-MiT benchmark. Our code is made publicly available at https://github.com/zhenglab/multi-action-video.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Shi_Multi-Modal_Multi-Action_Video_Recognition_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Shi_Multi-Modal_Multi-Action_Video_Recognition_ICCV_2021_paper.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":"multi-modal-multi-action-video-recognition","repo_url":"https://github.com/zhenglab/multi-action-video","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":null,"task_name":"Relation"},{"task_slug":"video-recognition","task_name":"Video Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}