{"type":"ScholarlyArticle","title":"Enhancing Cross-Modal Retrieval via Visual-Textual Prompt Hashing","authors":[{"name":"Bingzhi Chen","slug":"bingzhi-chen","given":"Bingzhi","family":"Chen","affiliation":"Beijing Institute of Technology , Zhuhai , China"},{"name":"Zhongqi Wu","slug":"zhongqi-wu","given":"Zhongqi","family":"Wu","affiliation":"South China Normal University , Guangzhou , China"},{"name":"Yishu Liu","slug":"yishu-liu","given":"Yishu","family":"Liu","affiliation":"Harbin Institute of Technology , Shenzhen , China"},{"name":"Biqing Zeng","slug":"biqing-zeng","given":"Biqing","family":"Zeng","affiliation":"South China Normal University , Guangzhou , China"},{"name":"Guangming Lu","slug":"guangming-lu","given":"Guangming","family":"Lu","affiliation":"Harbin Institute of Technology , Shenzhen , China"},{"name":"Zheng Zhang","slug":"zheng-zhang","given":"Zheng","family":"Zhang","affiliation":"Harbin Institute of Technology , Shenzhen , China"}],"abstract":"Cross-modal hashing has garnered considerable research interest due to its rapid retrieval and low storage costs. However, the majority of existing methods suffer from the limitations of context loss and information redundancy, particularly in simulated textual environments enriched with manually annotated tags or virtual descriptions. To mitigate these issues, we propose a novel Visual-Textual Prompt Hashing (VTPH) that aims to bridge the gap between simulated textual and visual modalities within a unified prompt optimization paradigm for cross-modal retrieval. By seamlessly integrating robust reasoning capabilities inherent in large-scale models, we design the visual and textual alignment prompt mechanisms to collaboratively enhance the contextual awareness and semantic capabilities embedded within simulated textual features. Furthermore, an affinity-adaptive contrastive learning strategy is dedicated to dynamically recalibrating the semantic interaction between visual and textual modalities by modeling the nuanced heterogeneity and semantic gaps between simulated and real-world textual environments. To the best of our knowledge, this is the first attempt to integrate both visual and textual prompt learning into cross-modal hashing, facilitating the efficacy of semantic coherence between diverse modalities. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority and robustness of our VTPH method over state-of-the-art competitors.","doi":"10.24963/ijcai.2024/69","url":"https://ijcai.org/proceedings/2024/69","pdf":"https://ijcai.org/proceedings/2024/0069.pdf","markdown":"https://ijcai.org/proceedings/2024/69.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/69","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":"Computer Vision","track":"Main Track","number":69,"pages":{"first":623,"last":631},"keywords":["Computer Vision: CV: Multimodal learning","Computer Vision: CV: Vision, language and reasoning","Computer Vision: CV: Scene analysis and understanding","Computer Vision: CV: Image and video retrieval"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2020] Cong Bai, Chao Zeng, Qing Ma, Jinglin Zhang, and Shengyong Chen. 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