{"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/building-a-large-scale-dataset-for-image","title":"Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark","arxiv_id":"1605.02677","date":"2016-05-09","proceeding":null,"authors":["Quanzeng You","Jiebo Luo","Hailin Jin","Jianchao Yang"],"abstract":"Psychological research results have confirmed that people can have different\nemotional reactions to different visual stimuli. Several papers have been\npublished on the problem of visual emotion analysis. In particular, attempts\nhave been made to analyze and predict people's emotional reaction towards\nimages. To this end, different kinds of hand-tuned features are proposed. The\nresults reported on several carefully selected and labeled small image data\nsets have confirmed the promise of such features. While the recent successes of\nmany computer vision related tasks are due to the adoption of Convolutional\nNeural Networks (CNNs), visual emotion analysis has not achieved the same level\nof success. This may be primarily due to the unavailability of confidently\nlabeled and relatively large image data sets for visual emotion analysis. In\nthis work, we introduce a new data set, which started from 3+ million weakly\nlabeled images of different emotions and ended up 30 times as large as the\ncurrent largest publicly available visual emotion data set. We hope that this\ndata set encourages further research on visual emotion analysis. We also\nperform extensive benchmarking analyses on this large data set using the state\nof the art methods including CNNs.","url_abs":"http://arxiv.org/abs/1605.02677v1","url_pdf":"http://arxiv.org/pdf/1605.02677v1.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":"building-a-large-scale-dataset-for-image","repo_url":"https://github.com/noahj08/DeepConnotation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"building-a-large-scale-dataset-for-image","repo_url":"https://github.com/pohlinwei/AComPianist","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1605.02677","atlas_url":"https://app.syntology.ai/?focus=1605.02677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.02677"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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