{"type":"ScholarlyArticle","title":"Cross-modal Generation and Alignment via Attribute-guided Prompt for Unsupervised Text-based Person Retrieval","authors":[{"name":"Zongyi Li","slug":"zongyi-li","given":"Zongyi","family":"Li","affiliation":"Huazhong University of Science and Technology , Wuhan , China"},{"name":"Jianbo Li","slug":"jianbo-li","given":"Jianbo","family":"Li","affiliation":"Huazhong University of Science and Technology , Wuhan , China"},{"name":"Yuxuan Shi","slug":"yuxuan-shi","given":"Yuxuan","family":"Shi","affiliation":"Huazhong University of Science and Technology , Wuhan , China"},{"name":"Hefei Ling","slug":"hefei-ling","given":"Hefei","family":"Ling","affiliation":"Huazhong University of Science and Technology , Wuhan , China"},{"name":"Jiazhong Chen","slug":"jiazhong-chen","given":"Jiazhong","family":"Chen","affiliation":"Huazhong University of Science and Technology , Wuhan , China"},{"name":"Runsheng Wang","slug":"runsheng-wang","given":"Runsheng","family":"Wang","affiliation":"Huazhong University of Science and Technology , Wuhan , China"},{"name":"Shijuan Huang","slug":"shijuan-huang","given":"Shijuan","family":"Huang","affiliation":"Huazhong University of Science and Technology , Wuhan , China"}],"abstract":"Text-based Person Search aims to retrieve a specified person using a given text query. Current methods predominantly rely on paired labeled image-text data to train the cross-modality retrieval model, necessitating laborious and time-consuming labeling. In response to this challenge, we present the Cross-modal Generation and Alignment via Attribute-guided Prompt framework (GAAP) for fully unsupervised text-based person search, utilizing only unlabeled images. Our proposed GAAP framework consists of two key parts: Attribute-guided Prompt Caption Generation and Attribute-guided Cross-modal Alignment module. The Attribute-guided Prompt Caption Generation module generates pseudo text labels by feeding the attribute prompts into a large-scale pre-trained vision-language model. These synthetic texts are then meticulously selected through a sample selection, ensuring the reliability for subsequent fine-tuning. The Attribute-guided Cross-modal Alignment module encompasses three sub-modules for feature alignment across modalities. Firstly, Cross-Modal Center Alignment (CMCA) aligns the samples with different modality centroids. Subsequently, to address ambiguity arising from local attribute similarities, an Attribute-guided Image-Text Contrastive Learning module (AITC) is proposed to facilitate the alignment of relationships among different pairs by considering local attribute similarities. Lastly, the Attribute-guided Image-Text Matching (AITM) module is introduced to mitigate noise in pseudo captions by using the image-attribute matching score to soften the hard matching labels. Empirical results showcase the effectiveness of our method across various text-based person search datasets under the fully unsupervised setting.","doi":"10.24963/ijcai.2024/116","url":"https://ijcai.org/proceedings/2024/116","pdf":"https://ijcai.org/proceedings/2024/0116.pdf","markdown":"https://ijcai.org/proceedings/2024/116.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/116","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":116,"pages":{"first":1047,"last":1055},"keywords":["Computer Vision: CV: Multimodal learning","Computer Vision: CV: Vision, language and reasoning","Machine Learning: ML: Unsupervised learning","Machine Learning: ML: Multi-modal learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2020] Surbhi Aggarwal, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty. 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