{"type":"ScholarlyArticle","title":"Multi-Attention Based Visual-Semantic Interaction for Few-Shot Learning","authors":[{"name":"Peng Zhao","slug":"peng-zhao","given":"Peng","family":"Zhao","affiliation":"School of Computer Science and Technology , Anhui University"},{"name":"Yin Wang","slug":"yin-wang","given":"Yin","family":"Wang","affiliation":"School of Computer Science and Technology , Anhui University"},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"},{"name":"Jie Mu","slug":"jie-mu","given":"Jie","family":"Mu","affiliation":"School of Data Science and Artificial Intelligence , Dongbei University of Finance and Economics"},{"name":"Huiting Liu","slug":"huiting-liu","given":"Huiting","family":"Liu","affiliation":"School of Computer Science and Technology , Anhui University"},{"name":"Cong Wang","slug":"cong-wang","given":"Cong","family":"Wang","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University; Department of Computing , The Hong Kong Polytechnic University"},{"name":"Xiaochun Cao","slug":"xiaochun-cao","given":"Xiaochun","family":"Cao","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"}],"abstract":"Few-Shot Learning (FSL) aims to train a model that can generalize to recognize new classes, with each new class having only very limited training samples. Since extracting discriminative features for new classes with few samples is challenging, existing FSL methods leverage visual and semantic prior knowledge to guide discriminative feature learning. However, for meta-learning purposes, the semantic knowledge of the query set is unavailable, so their features lack discriminability. To address this problem, we propose a novel Multi-Attention based Visual-Semantic Interaction (MAVSI) approach for FSL. Specifically, we utilize spatial and channel attention mechanisms to effectively select discriminative visual features for the support set based on its ground-truth semantics while using all the support set semantics for each query set sample. Then, a relation module with class prototypes of the support set is employed to supervise and select discriminative visual features for the query set. To further enhance the discriminability of the support set, we introduce a visual-semantic contrastive learning module to promote the similarity between visual features and their corresponding semantic features. Extensive experiments on four benchmark datasets demonstrate that our proposed MAVSI could outperform existing state-of-the-art FSL methods.","doi":"10.24963/ijcai.2024/194","url":"https://ijcai.org/proceedings/2024/194","pdf":"https://ijcai.org/proceedings/2024/0194.pdf","markdown":"https://ijcai.org/proceedings/2024/194.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/194","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":194,"pages":{"first":1753,"last":1761},"keywords":["Machine Learning: ML: Meta-learning","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Afham et al., 2021] Mohamed Afham, Salman Khan, Muhammad Haris Khan, Muzammal Naseer, and Fa- had Shahbaz Khan. 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