{"type":"ScholarlyArticle","title":"FineFMPL: Fine-grained Feature Mining Prompt Learning for Few-Shot Class Incremental Learning","authors":[{"name":"Hongbo Sun","slug":"hongbo-sun","given":"Hongbo","family":"Sun","affiliation":"Wangxuan Institute of Computer Technology , Peking University"},{"name":"Jiahuan Zhou","slug":"jiahuan-zhou","given":"Jiahuan","family":"Zhou","affiliation":"Wangxuan Institute of Computer Technology , Peking University"},{"name":"Xiangteng He","slug":"xiangteng-he","given":"Xiangteng","family":"He","affiliation":"Wangxuan Institute of Computer Technology , Peking University"},{"name":"Jinglin Xu","slug":"jinglin-xu","given":"Jinglin","family":"Xu","affiliation":"School of Intelligence Science and Technology , University of Science and Technology Beijing"},{"name":"Yuxin Peng","slug":"yuxin-peng","given":"Yuxin","family":"Peng","affiliation":"Wangxuan Institute of Computer Technology , Peking University"}],"abstract":"Few-Shot Class Incremental Learning (FSCIL) aims to continually learn new classes with few training samples without forgetting already learned old classes. Existing FSCIL methods generally fix the backbone network in incremental sessions to achieve a balance between suppressing forgetting old classes and learning new classes. However, the fixed backbone network causes insufficient learning of new classes from a few samples. Benefiting from the powerful visual and textual understanding ability of Vision-Language (VL) pre-training models, we propose a Fine-grained Feature Mining Prompt Learning (FineFMPL) approach to adapt the VL model to FSCIL, which comprehensively learns and memorizes fine-grained discriminative information of emerging classes. Concretely, the visual probe prompt is firstly proposed to guide the image encoder of VL model to extract global-level coarse-grained features and object-level fine-grained features, and visual prototypes are preserved based on image patch significance, which contains the discriminative characteristics exclusive to the class. Secondly, the textual context prompt is constructed by cross-modal mapping of visual prototypes, feeding into the text encoder of VL model to memorize the class information as textual prototypes. Finally, integrating visual and textual prototypes based on fine-grained feature mining into the model improves the recognition performance of all classes in FSCIL. Extensive experiments on three benchmark datasets demonstrate that our FineFMPL achieves new state-of-the-art. The code is available at https://github.com/PKU-ICST-MIPL/FineFMPL_IJCAI2024.","doi":"10.24963/ijcai.2024/144","url":"https://ijcai.org/proceedings/2024/144","pdf":"https://ijcai.org/proceedings/2024/0144.pdf","markdown":"https://ijcai.org/proceedings/2024/144.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/144","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":144,"pages":{"first":1299,"last":1307},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Abati et al., 2020] Davide Abati, Jakub Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, and Babak Ehteshami Bejnordi. 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