{"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/context-aware-emotion-recognition-networks","title":"Context-Aware Emotion Recognition Networks","arxiv_id":"1908.05913","date":"2019-08-16","proceeding":"ICCV 2019 10","authors":["Jiyoung Lee","Seungryong Kim","Sunok Kim","Jungin Park","Kwanghoon Sohn"],"abstract":"Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner. The key idea is to hide human faces in a visual scene and seek other contexts based on an attention mechanism. Our networks consist of two sub-networks, including two-stream encoding networks to seperately extract the features of face and context regions, and adaptive fusion networks to fuse such features in an adaptive fashion. We also introduce a novel benchmark for context-aware emotion recognition, called CAER, that is more appropriate than existing benchmarks both qualitatively and quantitatively. On several benchmarks, CAER-Net proves the effect of context for emotion recognition. Our dataset is available at http://caer-dataset.github.io.","url_abs":"https://arxiv.org/abs/1908.05913v1","url_pdf":"https://arxiv.org/pdf/1908.05913v1.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":"context-aware-emotion-recognition-networks","repo_url":"https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"emotion-recognition-in-context","task_name":"Emotion Recognition in Context"}],"methods":[],"datasets_introduced":[{"slug":"caer-dynamic","name":"CAER-Dynamic","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-classification-on-caer-dynamic","task":"Emotion Classification","dataset":"CAER-Dynamic","model":"CAERNet","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"77.04"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-context-on-caer-1","task":"Emotion Recognition in Context","dataset":"CAER","model":"CAER-Net-S","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"73.51"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-context-on-caer","task":"Emotion Recognition in Context","dataset":"CAER-Dynamic","model":"CAER-Net","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"77.04"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-context-on-emotic","task":"Emotion Recognition in Context","dataset":"EMOTIC","model":"CAER-Net (Adaptive Fusion)","rank_in_archive_order":9,"of":9,"metrics":{"mAP":"20.84"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1908.05913","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}