{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T16:10:27Z","timestamp":1756311027546},"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":[[2021,8]]},"abstract":"<jats:p>Recently, hashing is widely used in approximate nearest neighbor search for its storage and computational efficiency. Most of the unsupervised hashing methods learn to map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure from the pre-trained model as the guiding information, i.e., treating each point pair similar if their distance is small in feature space. However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning. Moreover, few of the methods consider the robustness of models, which will cause instability of hash codes to disturbance. In this paper, we propose a new method named  Comprehensive sImilarity Mining and cOnsistency learNing (CIMON). First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods in both retrieval performance and robustness.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/125","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"902-908","source":"Crossref","is-referenced-by-count":25,"title":["CIMON: Towards High-quality Hash Codes"],"prefix":"10.24963","author":[{"given":"Xiao","family":"Luo","sequence":"first","affiliation":[{"name":"Peking University"},{"name":"DAMO Academy, Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daqing","family":"Wu","sequence":"additional","affiliation":[{"name":"Peking University"},{"name":"DAMO Academy, Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyu","family":"Ma","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University"},{"name":"DAMO Academy, Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghua","family":"Deng","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwen","family":"Ma","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongming","family":"Jin","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianqiang","family":"Huang","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian-Sheng","family":"Hua","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:01:31Z","timestamp":1628679691000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/125"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/125","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}