{"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/human-centered-emotion-recognition-in","title":"Human-Centered Emotion Recognition in Animated GIFs","arxiv_id":"1904.12201","date":"2019-04-27","proceeding":null,"authors":["Zhengyuan Yang","Yixuan Zhang","Jiebo Luo"],"abstract":"As an intuitive way of expression emotion, the animated Graphical Interchange\nFormat (GIF) images have been widely used on social media. Most previous\nstudies on automated GIF emotion recognition fail to effectively utilize GIF's\nunique properties, and this potentially limits the recognition performance. In\nthis study, we demonstrate the importance of human related information in GIFs\nand conduct human-centered GIF emotion recognition with a proposed Keypoint\nAttended Visual Attention Network (KAVAN). The framework consists of a facial\nattention module and a hierarchical segment temporal module. The facial\nattention module exploits the strong relationship between GIF contents and\nhuman characters, and extracts frame-level visual feature with a focus on human\nfaces. The Hierarchical Segment LSTM (HS-LSTM) module is then proposed to\nbetter learn global GIF representations. Our proposed framework outperforms the\nstate-of-the-art on the MIT GIFGIF dataset. Furthermore, the facial attention\nmodule provides reliable facial region mask predictions, which improves the\nmodel's interpretability.","url_abs":"http://arxiv.org/abs/1904.12201v1","url_pdf":"http://arxiv.org/pdf/1904.12201v1.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":"human-centered-emotion-recognition-in","repo_url":"https://github.com/zyang-ur/human-centered-GIF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.12201","atlas_url":"https://app.syntology.ai/?focus=1904.12201","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}