{"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/semantic-structure-enhanced-contrastive","title":"Semantic Structure Enhanced Contrastive Adversarial Hash Network for Cross-media Representation Learning","arxiv_id":null,"date":"2022-10-22","proceeding":"ACM Multimedia 2022 10","authors":["Meiyu Liang","Junping Du","Xiaowen Cao","Yang Yu","Kangkang Lu","Zhe Xue","Min Zhang"],"abstract":"Deep cross-media hashing technology provides an efficient cross-media representation learning solution for cross-media search. However, the existing methods do not consider both fine-grained semantic features and semantic structures to mine implicit cross-media semantic associations, which leads to weaker semantic discrimination and consistency for cross-media representation. To tackle this problem, we propose a novel semantic structure enhanced contrastive adversarial hash network for cross-media representation learning (SCAHN). Firstly, in order to capture more fine-grained cross-media semantic associations, a fine-grained cross-media attention feature learning network is constructed, thus the learned saliency features of different modalities are more conducive to cross-media semantic alignment and fusion. Secondly, for further improving learning ability of implicit cross-media semantic associations, a semantic label association graph is constructed, and the graph convolutional network is utilized to mine the implicit semantic structures, thus guiding learning of discriminative features of different modalities. Thirdly, a cross-media and intra-media contrastive adversarial representation learning mechanism is proposed to further enhance the semantic discriminativeness of different modal representations, and a dual-way adversarial learning strategy is developed to maximize cross-media semantic associations, so as to obtain cross-media unified representations with stronger discriminativeness and semantic consistency preserving power. Extensive experiments on several cross-media benchmark datasets demonstrate that the proposed SCAHN outperforms the state-of-the-art methods.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3503161.3548391","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3503161.3548391","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":"semantic-structure-enhanced-contrastive","repo_url":"https://github.com/2023-MindSpore-1/ms-code-81","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"semantic-structure-enhanced-contrastive","repo_url":"https://github.com/lyqcom/SCAHN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}