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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>Sketch\u2013photo recognition refers to matching hand-drawn sketches with their corresponding photos, where the performance essentially depends on how well the representations of the two modalities are aligned in the feature spaces. Existing works bluntly force models to reduce the representation discrepancy between the modalities, making the learning less effective. Besides, the current symmetric feature extraction framework prefers the photo modality for richer information while neglecting the sketch modality. Driven by these observations, we argue that, instead of forcefully wiping out the modality discrepancy, we may utilize the discrepancy to enhance model learning. Thus, we propose a Cross-Modality Bootstrapping learning framework (CROMBO) that utilizes the modality discrepancy to bootstrap cross-modality representation learning via a differentiated interaction manner. Specifically, we first present a Sketch Implicit Bootstrapping (SIB) module to magnify the recognizable elements in the photo modality by utilizing the characteristic of sketches having only contours and key details. Second, a Photo-driven Sketch Refinement (PSR) module is developed to guide the sketch representation in the shared feature extraction process by supplementing rich information from the photo modality. Moreover, we design a second-order alignment strategy to dynamically align the latent distribution of two modalities in a Hilbert space. Also, our CROMBO can learn fewer parameters by freezing the weights of shallow layers in the backbone while making no sacrifice in performance. Extensive experiments on six public datasets verify the superior performance of our CROMBO for sketch\u2013photo-based tasks, such as sketch re-identification (Re-ID), sketch\u2013photo face recognition, and sketch-based image retrieval.<\/jats:p>","DOI":"10.1145\/3778043","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T15:05:52Z","timestamp":1764169552000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CROMBO: Cross-Modality Bootstrapping for Unified Sketch\u2013Photo Representation Learning"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6064-9104","authenticated-orcid":false,"given":"Xingyu","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2031-5627","authenticated-orcid":false,"given":"Yan","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2355-9010","authenticated-orcid":false,"given":"Xu","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8298-7181","authenticated-orcid":false,"given":"Hao","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Oulu, Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3267-2664","authenticated-orcid":false,"given":"Haoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Oulu, Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3694-206X","authenticated-orcid":false,"given":"Guoying","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Oulu, Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00423"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00107"},{"key":"e_1_3_1_4_2","first-page":"163","volume-title":"European Conference on Computer Vision","author":"Kumar Bhunia Ayan","year":"2022","unstructured":"Ayan Kumar Bhunia, Aneeshan Sain, Parth Hiren Shah, Animesh Gupta, Pinaki Nath Chowdhury, Tao Xiang, and Yi-Zhe Song. 2022. 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