{"type":"ScholarlyArticle","title":"Dual Semantic Fusion Hashing for Multi-Label Cross-Modal Retrieval","authors":[{"name":"Kaiming Liu","slug":"kaiming-liu","given":"Kaiming","family":"Liu","affiliation":"College of Computer Science , Sichuan University"},{"name":"Yunhong Gong","slug":"yunhong-gong","given":"Yunhong","family":"Gong","affiliation":"College of Computer Science , Sichuan University"},{"name":"Yu Cao","slug":"yu-cao","given":"Yu","family":"Cao","affiliation":"College of Computer Science , Sichuan University"},{"name":"Zhenwen Ren","slug":"zhenwen-ren","given":"Zhenwen","family":"Ren","affiliation":"School of National Defense Science and Technology , Southwest University of Science and Technology"},{"name":"Dezhong Peng","slug":"dezhong-peng","given":"Dezhong","family":"Peng","affiliation":"College of Computer Science , Sichuan University; National Innovation Center for UHD Video Technology"},{"name":"Yuan Sun","slug":"yuan-sun","given":"Yuan","family":"Sun","affiliation":"College of Computer Science , Sichuan University"}],"abstract":"Cross-modal hashing (CMH) has been widely used for multi-modal retrieval tasks due to its low storage cost and fast query speed. Although existing CMH methods achieve promising performance, most of them mainly rely on coarse-grained supervision information (\\ie pairwise similarity matrix) to measure the semantic similarities between all instances, ignoring the impact of multi-label distribution. To address this issue, we construct fine-grained semantic similarity to explore the cluster-level semantic relationships between multi-label data, and propose a new dual semantic fusion hashing (DSFH) for multi-label cross-modal retrieval. Specifically, we first learn the modal-specific representation and consensus hash codes, thereby merging the specificity with consistency. Then, we fuse the coarse-grained and fine-grained semantics to mine multiple-level semantic relationships, thereby enhancing hash codes discrimination. Extensive experiments on three benchmarks demonstrate the superior performance of our DSFH compared with 16 state-of-the-art methods.","doi":"10.24963/ijcai.2024/505","url":"https://ijcai.org/proceedings/2024/505","pdf":"https://ijcai.org/proceedings/2024/0505.pdf","markdown":"https://ijcai.org/proceedings/2024/505.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/505","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":505,"pages":{"first":4569,"last":4577},"keywords":["Machine Learning: ML: Multi-modal learning","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2020] Cong Bai, Chao Zeng, Qing Ma, Jinglin Zhang, and Shengyong Chen. 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