Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；Pengcheng Laboratory（鹏城实验室）；King Abdullah University of Science and Technology（国王阿卜杜勒-阿齐兹大学）；Department of Computer Science and Engineering, University of California, Merced（加州大学默塞德分校计算机科学与工程系）
Xiaojie Li, Yibo Yang, Jianlong Wu, Yue Yu, Ming-Hsuan Yang, Liqiang Nie, Min Zhang
Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples while preserving knowledge of previously learned classes. Existing methods face a critical dilemma: static architectures rely on a constant parameter space to learn from data that arrive sequentially, making them prone to overfitting to the current session, while dynamic architectures continually expand the parameter space, leading to increased complexity. In this study, we explore the potential of Selective State Space Models (SSMs) for FSCIL. Mamba leverages its input-dependent parameters to dynamically adjust its processing patterns and generate content-aware scan patterns without session-wise projector expansion. This enables it to configure distinct processing for base and novel classes, helping preserve existing knowledge while adapting to new ones. To leverage Mamba's potential for FSCIL, we design two key modules: First, we propose a dual selective SSM projector that generates input-conditioned state-space parameters from intermediate features for dynamic adaptation. The dual design structurally decouples base and novel-class processing, employing a frozen base branch to maintain stable base-class features and a dynamic incremental branch that adaptively learns distinctive feature shifts for novel classes. Second, we develop a class-sensitive selective scan mechanism to guide dynamic adaptation of the incremental branch. It reduces the disruption to base-class representations caused by training on novel data, and meanwhile, encourages the selective scan to perform in distinct patterns between base and novel classes. Extensive experiments on miniImageNet, CIFAR-100, and CUB-200 demonstrate that Mamba-FSCIL achieves state-of-the-art performance.
Few-shot Class-incremental Learning for 3D Point Cloud Objects
North South University（北南大学）；Australian National University（澳大利亚国立大学）；Commonwealth Scientific and Industrial Research Organisation（澳大利亚联邦科学与工业研究组织）；University of Adelaide（阿德莱德大学）；The University of New South Wales（新南威尔士大学）；University of Technology Sydney（悉尼科技大学）
Learning to Curate What You Generate for Generalizable Few-Shot Class-Incremental Learning
University of Science and Technology Beijing（北京科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Singapore Management University（新加坡管理大学）
Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；Old Dominion University（奥尔多明尼昂大学）；School of Integrated Circuits, University of Chinese Academy of Sciences（中国科学院大学集成电路学院）
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Australian National University（澳大利亚国立大学）；Data61-CSIRO Australia（澳大利亚联邦科学与工业研究组织Data61）；North South University Bangladesh（孟加拉国南北大学）；Monash University Australia（澳大利亚莫纳什大学）
Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning
New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别新实验室）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Electronics and Information Engineering, Suzhou University of Science and Technology（苏州科技大学电子与信息工程学院）；Jiangsu Industrial Intelligent Low Carbon Technology Engineering Center（江苏省工业智能低碳技术工程中心）
Adaptive Decision Boundary for Few-Shot Class-Incremental Learning
School of Artificial Intelligence and Data Science, Hebei University of Technology（河北工业大学人工智能与数据科学学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Two-level Graph Network for Few-Shot Class-Incremental Learning
Suzhou University of Science and Technology（苏州科技大学）；Suzhou Institute of Trade & Commerce（苏州经贸职业技术学院）；Tianjin University（天津大学）；Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center（江苏省工业智能与低碳技术工程中心）；Suzhou Key Laboratory of Intelligent Low-carbon Technology Application（苏州市智能低碳技术应用重点实验室）
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
National University of Defense Technology（国防科技大学）；University of Oulu（奥卢大学）；Nanyang Technological University（南洋理工大学）；Stanford University（斯坦福大学）
Few-Shot类增量学习（Class-Incremental Learning，FSCIL）的目标是从少量的标记样本中增量学习新类，同时避免过拟合和灾难性遗忘。FSCIL的当前协议是通过模仿一般的类增量学习设置来构建的，但是由于不同的数据配置，即，新的类都在有限的数据范围内。在本文中，我们重新考虑配置的FSCIL与开集假设保留的可能性，在第一届会议传入的类别。为了提高模型的闭集和开集识别性能，在倒易点学习（Reciprocal Point Learning，RPL）的基础上，利用双曲神经网络构建了双曲倒易点学习模块（Hyper-RPL）。此外，为了从有限的标记数据中学习新的类别，我们将双曲度量学习（Hyper-Metric）模块纳入基于蒸馏的框架中，以减轻过拟合问题，并更好地处理旧知识的保存和新知识的获取之间的权衡问题。在三个基准数据集上对所提出的配置和模块进行了全面评估，以验证三个评估指标的有效性。
CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning
Chinese Academy of Sciences（中国科学院）；Southeast University（东南大学）；School of electronic & information engineering, Suzhou University of Science and Technology（苏州科技大学电子与信息工程学院）；Tencent Youtu Lab（腾讯优图实验室）；North Minzu University（北方民族大学）
Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor
University of Technology Sydney（悉尼科技大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；Australian National University（澳大利亚国立大学）；North South University（南北大学）；York University（约克大学）
本文提出一种基于3D基础模型且无需训练的适配器方法，利用双重缓存系统防止遗忘与过拟合，有效解决3D点云环境下的Few-Shot Class Incremental Learning问题，在多个数据集上表现优异。
Sculpting Margin Penalty: Intra-Task Adapter Merging and Classifier Calibration for Few-Shot Class-Incremental Learning
School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Optics and Photonics, Beijing Institute of Technology（光学与 photonics 学院，北京理工大学）；School of Medical Technology, Beijing Institute of Technology（医学技术学院，北京理工大学）
Brain-inspired analogical mixture prototypes for few-shot class-incremental learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
Beihang University, Beijing 100191, China（北航，北京100191，中国）；Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China（航天信息研究所，中国科学院，北京100190，中国）
A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill Recognition
National University of Defense Technology (NUDT)（国防科技大学）；College of Intelligence Science and Technology（智能科学与技术学院）；College of Electronic Science and Technology（电子科学与技术学院）
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of Computer Science, Peking University（北京大学计算机学院）
