{"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/transformer-for-emotion-recognition","title":"Transformer for Emotion Recognition","arxiv_id":"1805.02489","date":"2018-05-03","proceeding":null,"authors":["Jean-Benoit Delbrouck"],"abstract":"This paper describes the UMONS solution for the OMG-Emotion Challenge. We\nexplore a context-dependent architecture where the arousal and valence of an\nutterance are predicted according to its surrounding context (i.e. the\npreceding and following utterances of the video). We report an improvement when\ntaking into account context for both unimodal and multimodal predictions.","url_abs":"http://arxiv.org/abs/1805.02489v2","url_pdf":"http://arxiv.org/pdf/1805.02489v2.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":"transformer-for-emotion-recognition","repo_url":"https://github.com/jbdel/OMG_UMONS_submission","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion 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}