{"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/listen-to-look-action-recognition-by","title":"Listen to Look: Action Recognition by Previewing Audio","arxiv_id":"1912.04487","date":"2019-12-10","proceeding":"CVPR 2020 6","authors":["Ruohan Gao","Tae-Hyun Oh","Kristen Grauman","Lorenzo Torresani"],"abstract":"In the face of the video data deluge, today's expensive clip-level classifiers are increasingly impractical. We propose a framework for efficient action recognition in untrimmed video that uses audio as a preview mechanism to eliminate both short-term and long-term visual redundancies. First, we devise an ImgAud2Vid framework that hallucinates clip-level features by distilling from lighter modalities---a single frame and its accompanying audio---reducing short-term temporal redundancy for efficient clip-level recognition. Second, building on ImgAud2Vid, we further propose ImgAud-Skimming, an attention-based long short-term memory network that iteratively selects useful moments in untrimmed videos, reducing long-term temporal redundancy for efficient video-level recognition. Extensive experiments on four action recognition datasets demonstrate that our method achieves the state-of-the-art in terms of both recognition accuracy and speed.","url_abs":"https://arxiv.org/abs/1912.04487v3","url_pdf":"https://arxiv.org/pdf/1912.04487v3.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":"listen-to-look-action-recognition-by","repo_url":"https://github.com/facebookresearch/Listen-to-Look","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"CC-BY-4.0"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"}],"methods":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-recognition-in-videos-on-activitynet","task":"Action Recognition","dataset":"ActivityNet","model":"ListenToLook","rank_in_archive_order":8,"of":16,"metrics":{"mAP":"89.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1912.04487","atlas_url":"https://app.syntology.ai/?focus=1912.04487","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}