{"type":"ScholarlyArticle","title":"TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Prompt","authors":[{"name":"Xiangyu Wu","slug":"xiangyu-wu","given":"Xiangyu","family":"Wu","affiliation":"Nanjing University of Science and Technology; Alibaba International Digital Commerce Group"},{"name":"Qing-Yuan Jiang","slug":"qing-yuan-jiang","given":"Qing-Yuan","family":"Jiang","affiliation":"Nanjing University of Science and Technology"},{"name":"Yang Yang","slug":"yang-yang","given":"Yang","family":"Yang","affiliation":"Nanjing University of Science and Technology"},{"name":"Yi-Feng Wu","slug":"yi-feng-wu","given":"Yi-Feng","family":"Wu","affiliation":"Alibaba International Digital Commerce Group"},{"name":"Qing-Guo Chen","slug":"qing-guo-chen","given":"Qing-Guo","family":"Chen","affiliation":"Alibaba International Digital Commerce Group"},{"name":"Jianfeng Lu","slug":"jianfeng-lu","given":"Jianfeng","family":"Lu","affiliation":"Nanjing University of Science and Technology"}],"abstract":"The recent introduction of prompt tuning based on pre-trained vision-language models has dramatically improved the performance of multi-label image classification. However, some existing strategies that have been explored still have drawbacks, i.e., either exploiting massive labeled visual data at a high cost or using text data only for text prompt tuning and thus failing to learn the diversity of visual knowledge. Hence, the application scenarios of these methods are limited. In this paper, we propose a pseudo-visual prompt (PVP) module for implicit visual prompt tuning to address this problem. Specifically, we first learn the pseudo-visual prompt for each category, mining diverse visual knowledge by the well-aligned space of pre-trained vision-language models. Then, a co-learning strategy with a dual-adapter module is designed to transfer visual knowledge from pseudo-visual prompt to text prompt, enhancing their visual representation abilities. Experimental results on VOC2007, MS-COCO, and NUSWIDE datasets demonstrate that our method can surpass state-of-the-art (SOTA) methods across various settings for multi-label image classification tasks. The code is available at https://github.com/njustkmg/PVP.","doi":"10.24963/ijcai.2024/578","url":"https://ijcai.org/proceedings/2024/578","pdf":"https://ijcai.org/proceedings/2024/0578.pdf","markdown":"https://ijcai.org/proceedings/2024/578.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/578","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":578,"pages":{"first":5226,"last":5234},"keywords":["Machine Learning: ML: Multi-label learning","Machine Learning: ML: Multi-modal learning","Computer Vision: CV: Multimodal learning"],"videos":[],"references":[{"position":1,"raw":"[Alfassy et al., 2019] Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogério Schmidt Feris, Raja Giryes, and Alexander M. 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