{"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/contrastive-positive-sample-propagation-along","title":"Contrastive Positive Sample Propagation along the Audio-Visual Event Line","arxiv_id":"2211.09980","date":"2022-11-18","proceeding":null,"authors":["Jinxing Zhou","Dan Guo","Meng Wang"],"abstract":"Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments containing an AVE and identify its category. It is pivotal to learn the discriminative features for each video segment. Unlike existing work focusing on audio-visual feature fusion, in this paper, we propose a new contrastive positive sample propagation (CPSP) method for better deep feature representation learning. The contribution of CPSP is to introduce the available full or weak label as a prior that constructs the exact positive-negative samples for contrastive learning. Specifically, the CPSP involves comprehensive contrastive constraints: pair-level positive sample propagation (PSP), segment-level and video-level positive sample activation (PSA$_S$ and PSA$_V$). Three new contrastive objectives are proposed (\\emph{i.e.}, $\\mathcal{L}_{\\text{avpsp}}$, $\\mathcal{L}_\\text{spsa}$, and $\\mathcal{L}_\\text{vpsa}$) and introduced into both the fully and weakly supervised AVE localization. To draw a complete picture of the contrastive learning in AVE localization, we also study the self-supervised positive sample propagation (SSPSP). As a result, CPSP is more helpful to obtain the refined audio-visual features that are distinguishable from the negatives, thus benefiting the classifier prediction. Extensive experiments on the AVE and the newly collected VGGSound-AVEL100k datasets verify the effectiveness and generalization ability of our method.","url_abs":"https://arxiv.org/abs/2211.09980v1","url_pdf":"https://arxiv.org/pdf/2211.09980v1.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":"contrastive-positive-sample-propagation-along","repo_url":"https://github.com/jasongief/cpsp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.09980","atlas_url":"https://app.syntology.ai/?focus=2211.09980","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}