{"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/automatic-attribute-discovery-with-neural","title":"Automatic Attribute Discovery with Neural Activations","arxiv_id":"1607.07262","date":"2016-07-25","proceeding":null,"authors":["Sirion Vittayakorn","Takayuki Umeda","Kazuhiko Murasaki","Kyoko Sudo","Takayuki Okatani","Kota Yamaguchi"],"abstract":"How can a machine learn to recognize visual attributes emerging out of online\ncommunity without a definitive supervised dataset? This paper proposes an\nautomatic approach to discover and analyze visual attributes from a noisy\ncollection of image-text data on the Web. Our approach is based on the\nrelationship between attributes and neural activations in the deep network. We\ncharacterize the visual property of the attribute word as a divergence within\nweakly-annotated set of images. We show that the neural activations are useful\nfor discovering and learning a classifier that well agrees with human\nperception from the noisy real-world Web data. The empirical study suggests the\nlayered structure of the deep neural networks also gives us insights into the\nperceptual depth of the given word. Finally, we demonstrate that we can utilize\nhighly-activating neurons for finding semantically relevant regions.","url_abs":"http://arxiv.org/abs/1607.07262v1","url_pdf":"http://arxiv.org/pdf/1607.07262v1.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":"automatic-attribute-discovery-with-neural","repo_url":"https://github.com/AemikaChow/DATASOURCE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.07262","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}