{"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/integrating-multi-label-contrastive-learning","title":"Integrating multi-label contrastive learning with dual adversarial graph neural networks for cross-modal retrieval","arxiv_id":null,"date":"2022-07-05","proceeding":"IEEE Transactions on Pattern Analysis and Machine Intelligence 2022 7","authors":["Dizhan Xue","Shengsheng Qian","Quan Fang","Changsheng Xu"],"abstract":"With the growing amount of multimodal data, cross-modal retrieval has attracted more and more attention and become a hot research topic. To date, most of the existing techniques mainly convert multimodal data into a common representation space where similarities in semantics between samples can be easily measured across multiple modalities. However, these approaches may suffer from the following limitations: 1) They overcome the modality gap by introducing loss in the common representation space, which may not be sufficient to eliminate the heterogeneity of various modalities; 2) They treat labels as independent entities and ignore label relationships, which is not conducive to establishing semantic connections across multimodal data; 3) They ignore the non-binary values of label similarity in multi-label scenarios, which may lead to inefficient alignment of representation similarity with label similarity. To tackle these problems, in this article, we propose two models to learn discriminative and modality-invariant representations for cross-modal retrieval. First, the dual generative adversarial networks are built to project multimodal data into a common representation space. Second, to model label relation dependencies and develop inter-dependent classifiers, we employ multi-hop graph neural networks (consisting of Probabilistic GNN and Iterative GNN), where the layer aggregation mechanism is suggested for using propagation information of various hops. Third, we propose a novel soft multi-label contrastive loss for cross-modal retrieval, with the soft positive sampling probability, which can align the representation similarity and the label similarity. Additionally, to adapt to incomplete-modal learning, which can have wider applications, we propose a modal reconstruction mechanism to generate missing features. Extensive experiments on three widely used benchmark datasets, i.e., NUS-WIDE, MIRFlickr, and MS-COCO, show the superiority of our proposed method.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9815553/","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9815553","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":"integrating-multi-label-contrastive-learning","repo_url":"https://github.com/LivXue/GNN4CMR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"cross-modal-retrieval","task_name":"Cross-Modal Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}