{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T21:49:50Z","timestamp":1757540990623},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Cross-view retrieval, which focuses on searching images as response to text queries or vice versa, has received increasing attention recently. Cross-view hashing is to efficiently solve the cross-view retrieval problem with binary hash codes. Most existing works on cross-view hashing exploit multi-view embedding method to tackle this problem, which inevitably causes the information loss in both image and text domains. Inspired by the Generative Adversarial Nets (GANs), this paper presents a new model that is able to Turn Cross-view Hashing into single-view hashing (TUCH), thus enabling the information of image to be preserved as much as possible. TUCH is a novel deep architecture that integrates a language model network T for text feature extraction, a generator network G to generate fake images from text feature and a hashing network H for learning hashing functions to generate compact binary codes. Our architecture effectively unifies joint generative adversarial learning and cross-view hashing. Extensive empirical evidence shows that our TUCH approach achieves state-of-the-art results, especially on text to image retrieval, based on image-sentences datasets, i.e. standard IAPRTC-12 and large-scale Microsoft COCO.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/491","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"3511-3517","source":"Crossref","is-referenced-by-count":10,"title":["TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets"],"prefix":"10.24963","author":[{"given":"Xin","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Software, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiguang","family":"Ding","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchen","family":"Guo","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jungong","family":"Han","sequence":"additional","affiliation":[{"name":"Lancaster University, Lancaster, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:54:10Z","timestamp":1501242850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/491"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/491","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}