{"type":"ScholarlyArticle","title":"DSSL-Hash: Dynamic Semantic Structure Learning for Unsupervised Cross-Modal Hashing","authors":[{"name":"Fan Yang","slug":"fan-yang","given":"Fan","family":"Yang","affiliation":""},{"name":"Tongxuan Pei","slug":"tongxuan-pei","given":"Tongxuan","family":"Pei","affiliation":""},{"name":"Yuanzhi Zhao","slug":"yuanzhi-zhao","given":"Yuanzhi","family":"Zhao","affiliation":""},{"name":"Yudong Zhao","slug":"yudong-zhao","given":"Yudong","family":"Zhao","affiliation":""}],"abstract":"A central bottleneck in unsupervised cross-modal hashing is that both similarity guidance and objective weighting are typically static, while the semantic structure induced during training is inherently dynamic. We present DSSL-Hash, which turns semantic structure learning into an evolving process rather than a one-shot preprocessing step. Specifically, DSSL-Hash distills semantic cues from a vision–language foundation model  and constructs a dynamic perception graph via an adaptive similarity matrix, enabling graph propagation to capture higher-order relations beyond pairwise matching. We further reshape Hamming-space learning with a semantic-channel constraint that explicitly regulates distances for partially similar pairs, reducing semantic distortion after binarization. Finally, we develop a Retrieval-Aware Adaptive Scheduler (RAAS) that leverages retrieval feedback to co-adjust modality interactions and objective weights, achieving robust optimization without extensive manual hyper-parameter search. Extensive experiments on MS COCO, NUS-WIDE, and MIRFLICKR-25K across multiple code lengths demonstrate that DSSL-Hash consistently outperforms recent state-of-the-art unsupervised cross-modal hashing methods.","doi":"10.24963/ijcai.2026/219","url":"https://ijcai.org/proceedings/2026/219","pdf":"https://ijcai.org/proceedings/2026/0219.pdf","markdown":"https://ijcai.org/proceedings/2026/219.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/219","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":219,"pages":{"first":1965,"last":1973},"keywords":["Machine Learning: Multi-modal learning","Natural Language Processing: Information retrieval and text mining","Computer Vision: Image and video retrieval","Data Mining: Information retrieval","Machine Learning: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}