{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:51Z","timestamp":1758672891320,"version":"3.44.0"},"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":[[2025,9]]},"abstract":"<jats:p>Multimodal hashing projects multimodal data into compact binary codes, enabling rapid and storage-efficient retrieval of large-scale multimedia content. \n\nIn practical scenarios, the issue of missing modality frequently arises when dealing with multimodal data.\n\nExisting incomplete multimodal hashing techniques directly recover missing modalities by neural networks, resulting in a disjointed representation space between the recovered and true data. \n\nIn this paper, we present a novel recovery paradigm, namely Prototype-based Modality Completion Hashing (PMCH). \n\nInstead of directly synthesizing it from available modalities, PMCH adaptively aggregates associated within-modality prototypes to recover missing modality data.\n\nSpecifically, PMCH introduces an within-modality prototype learning module to optimize representative prototypes for each modality. \n\nThese prototypes act as recovery anchors and reside within the same representation space as their corresponding modality data. \n\nSubsequently, PMCH adaptively aggregates the associated within-modality prototypes with coefficients derived from the modality-specific Weight-Net.\n\nBy utilizing prototypes from the same modality, the semantic disparity between the reconstructed and authentic data can be substantially diminished.\n\nExtensive experiments on three widely used benchmark datasets demonstrate that PMCH can effectively recover the missing modality, and attain state-of-the-art performance in both complete and incomplete multimodal retrieval scenarios. Code is available at https:\/\/github.com\/Sasa77777779\/PMCH.git.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/281","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"2521-2529","source":"Crossref","is-referenced-by-count":0,"title":["Endogenous Recovery via Within-modality Prototypes for Incomplete Multimodal Hashing"],"prefix":"10.24963","author":[{"given":"Sa","family":"Zhu","sequence":"first","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences"},{"name":"School of Cyber Security, University of Chinese Academy of Sciences"},{"name":"State Key Laboratory of Cyberspace Security Defense"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dayan","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenming","family":"Wu","sequence":"additional","affiliation":[{"name":"Baidu Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengwen","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences"},{"name":"State Key Laboratory of Cyberspace Security Defense"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:36Z","timestamp":1758627216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/281"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/281","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}