{"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/deep-co-clustering-for-unsupervised","title":"Deep Multimodal Clustering for Unsupervised Audiovisual Learning","arxiv_id":"1807.03094","date":"2018-07-09","proceeding":"CVPR 2019 6","authors":["Di Hu","Feiping Nie","Xuelong. Li"],"abstract":"The seen birds twitter, the running cars accompany with noise, etc. These\nnaturally audiovisual correspondences provide the possibilities to explore and\nunderstand the outside world. However, the mixed multiple objects and sounds\nmake it intractable to perform efficient matching in the unconstrained\nenvironment. To settle this problem, we propose to adequately excavate audio\nand visual components and perform elaborate correspondence learning among them.\nConcretely, a novel unsupervised audiovisual learning model is proposed, named\nas \\Deep Multimodal Clustering (DMC), that synchronously performs sets of\nclustering with multimodal vectors of convolutional maps in different shared\nspaces for capturing multiple audiovisual correspondences. And such integrated\nmultimodal clustering network can be effectively trained with max-margin loss\nin the end-to-end fashion. Amounts of experiments in feature evaluation and\naudiovisual tasks are performed. The results demonstrate that DMC can learn\neffective unimodal representation, with which the classifier can even\noutperform human performance. Further, DMC shows noticeable performance in\nsound localization, multisource detection, and audiovisual understanding.","url_abs":"http://arxiv.org/abs/1807.03094v3","url_pdf":"http://arxiv.org/pdf/1807.03094v3.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":"deep-co-clustering-for-unsupervised","repo_url":"https://github.com/DTaoo/Simplified_DMC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}