{"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/self-supervised-adversarial-hashing-networks","title":"Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval","arxiv_id":"1804.01223","date":"2018-04-04","proceeding":"CVPR 2018 6","authors":["Chao Li","Cheng Deng","Ning li","Wei Liu","Xinbo Gao","DaCheng Tao"],"abstract":"Thanks to the success of deep learning, cross-modal retrieval has made\nsignificant progress recently. However, there still remains a crucial\nbottleneck: how to bridge the modality gap to further enhance the retrieval\naccuracy. In this paper, we propose a self-supervised adversarial hashing\n(\\textbf{SSAH}) approach, which lies among the early attempts to incorporate\nadversarial learning into cross-modal hashing in a self-supervised fashion. The\nprimary contribution of this work is that two adversarial networks are\nleveraged to maximize the semantic correlation and consistency of the\nrepresentations between different modalities. In addition, we harness a\nself-supervised semantic network to discover high-level semantic information in\nthe form of multi-label annotations. Such information guides the feature\nlearning process and preserves the modality relationships in both the common\nsemantic space and the Hamming space. Extensive experiments carried out on\nthree benchmark datasets validate that the proposed SSAH surpasses the\nstate-of-the-art methods.","url_abs":"http://arxiv.org/abs/1804.01223v1","url_pdf":"http://arxiv.org/pdf/1804.01223v1.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":"self-supervised-adversarial-hashing-networks","repo_url":"https://github.com/lelan-li/SSAH","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"cross-modal-retrieval","task_name":"Cross-Modal Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.01223","atlas_url":"https://app.syntology.ai/?focus=1804.01223","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}