{"type":"ScholarlyArticle","title":"With a Little Help from Language: Semantic Enhanced Visual Prototype Framework for Few-Shot Learning","authors":[{"name":"Hecheng Cai","slug":"hecheng-cai","given":"Hecheng","family":"Cai","affiliation":"College of Computer Science , Sichuan University , Chengdu 610065 , China; Engineering Research Center of Machine Learning and Industry Intelligence , Ministry of Education , Chengdu , 610065 China"},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"College of Computer Science , Sichuan University , Chengdu 610065 , China; Engineering Research Center of Machine Learning and Industry Intelligence , Ministry of Education , Chengdu , 610065 China"},{"name":"Shudong Huang","slug":"shudong-huang","given":"Shudong","family":"Huang","affiliation":"College of Computer Science , Sichuan University , Chengdu 610065 , China; Engineering Research Center of Machine Learning and Industry Intelligence , Ministry of Education , Chengdu , 610065 China"},{"name":"Jiancheng Lv","slug":"jiancheng-lv","given":"Jiancheng","family":"Lv","affiliation":"College of Computer Science , Sichuan University , Chengdu 610065 , China; Engineering Research Center of Machine Learning and Industry Intelligence , Ministry of Education , Chengdu , 610065 China"}],"abstract":"Few-shot learning (FSL) aims to recognize new categories given limited training samples. The core challenge is to avoid overfitting to the minimal data while ensuring good generalization to novel classes. One mainstream method employs prototypes from visual feature extractors as classifier weight and the performance depends on the quality of the prototype. Since different categories may have similar visual features, the visual prototype has limitations. This is because existing methods only learn a simple visual feature extractor during the pre-training stage but neglect the importance of a well-developed feature space for the prototype. We introduce the Semantic Enhanced Visual Prototype framework (SEVpro) to address this issue. SEVpro refines prototype learning from the pre-training stage and serves as a versatile plug-and-play framework for all prototype-based FSL methods. Specifically, we enhance prototype discriminability by transforming semantic embeddings into the visual space, aiding in separating categories with similar visual features. For novel class learning, we leverage knowledge from base classes and incorporate semantic information to elevate prototype quality further. Meanwhile, extensive experiments on FSL benchmarks and ablation studies demonstrate the superiority of our proposed SEVpro for FSL.","doi":"10.24963/ijcai.2024/415","url":"https://ijcai.org/proceedings/2024/415","pdf":"https://ijcai.org/proceedings/2024/0415.pdf","markdown":"https://ijcai.org/proceedings/2024/415.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/415","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":415,"pages":{"first":3751,"last":3759},"keywords":["Machine Learning: ML: Multi-modal learning","Machine Learning: ML: Few-shot learning"],"videos":[],"references":[{"position":1,"raw":"[Bertinetto et al., 2018] Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi. 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