{"type":"ScholarlyArticle","title":"BMIP: Bi-directional Modality Interaction Prompt Learning for VLM","authors":[{"name":"Song-Lin Lv","slug":"song-lin-lv","given":"Song-Lin","family":"Lv","affiliation":""},{"name":"Yu-Yang Chen","slug":"yu-yang-chen","given":"Yu-Yang","family":"Chen","affiliation":""},{"name":"Zhi Zhou","slug":"zhi-zhou","given":"Zhi","family":"Zhou","affiliation":""},{"name":"Ming Yang","slug":"ming-yang","given":"Ming","family":"Yang","affiliation":""},{"name":"Lan-Zhe Guo","slug":"lan-zhe-guo","given":"Lan-Zhe","family":"Guo","affiliation":""}],"abstract":"Vision-language models (VLMs) have exhibited remarkable generalization capabilities, and prompt learning for VLMs has attracted great attention for the ability to adapt pre-trained VLMs to specific downstream tasks. However, existing studies mainly focus on single-modal prompts or uni-directional modality interaction, overlooking the powerful alignment effects resulting from the interaction between the vision and language modalities. To this end, we propose a novel prompt learning method called Bi-directional Modality Interaction Prompt (BMIP), which dynamically weights bi-modal information through learning the information of the attention layer, enhancing trainability and inter-modal consistency compared to simple information aggregation methods. To evaluate the effectiveness of prompt learning methods, we propose a more realistic evaluation paradigm called open-world generalization complementing the widely adopted cross-dataset transfer and domain generalization tasks. Comprehensive experiments on various datasets reveal that BMIP not only outperforms current state-of-the-art methods across all three evaluation paradigms but is also flexible enough to be combined with other prompt-based methods for consistent performance enhancement.","doi":"10.24963/ijcai.2025/655","url":"https://ijcai.org/proceedings/2025/655","pdf":"https://ijcai.org/proceedings/2025/0655.pdf","markdown":"https://ijcai.org/proceedings/2025/655.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/655","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":655,"pages":{"first":5887,"last":5895},"keywords":["Computer Vision: CV: Multimodal learning","Machine Learning: ML: Multi-modal learning","Machine Learning: ML: Few-shot learning","Machine Learning: ML: Open-World/Open-Set/OOD Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}