{"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-task-learning-for-audio-visual-active","title":"Multi-Task Learning for Audio Visual Active Speaker Detection","arxiv_id":null,"date":"2019-06-01","proceeding":"The ActivityNet Large-Scale Activity Recognition Challenge Workshop, CVPR 2019 6","authors":["Yuanhang Zhang","Jingyun Xiao","Shuang Yang","Shiguang Shan"],"abstract":"This report describes the approach underlying our submission to the active speaker detection task (task B-2) of ActivityNet Challenge 2019. We introduce a new audio-visual model which builds upon a 3D-ResNet18 visual model pretrained for lipreading and a VGG-M acoustic model pretrained for audio-to-video synchronization. The model is trained with two losses in a multi-task learning fashion: a contrastive loss to enforce matching between audio and video features for active speakers, and a regular crossentropy loss to obtain speaker / non-speaker labels. This model obtains 84.0% mAP on the validation set of AVAActiveSpeaker. Experimental results showcase the pretrained embeddings' abilities to transfer across tasks and data formats, as well as the advantage of the proposed multi-task learning strategy.","url_abs":"http://research.google.com/ava/2019/Multi_Task_Learning_for_Audio_Visual_Active_Speaker_Detection.pdf","url_pdf":"http://research.google.com/ava/2019/Multi_Task_Learning_for_Audio_Visual_Active_Speaker_Detection.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":"active-speaker-detection","task_name":"Active Speaker Detection"},{"task_slug":"audio-visual-active-speaker-detection","task_name":"Audio-Visual Active Speaker Detection"},{"task_slug":"lipreading","task_name":"Lipreading"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"video-synchronization","task_name":"Video Synchronization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/audio-visual-active-speaker-detection-on-ava","task":"Audio-Visual Active Speaker Detection","dataset":"AVA-ActiveSpeaker","model":"3D-ResNet-GRU","rank_in_archive_order":20,"of":20,"metrics":{"validation mean average precision":"84.0%"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}