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Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class Incremental Learning
Summary
This study introduces Mamba-FSCIL, a novel approach for few-shot class-incremental learning (FSCIL) that effectively preserves existing knowledge while learning new classes. Mamba-FSCIL utilizes Selective State Space Models (SSMs) to dynamically adapt processing without expanding model complexity.
Area of Science:
- Machine Learning
- Computer Vision
- Artificial Intelligence
Background:
- Few-shot class-incremental learning (FSCIL) presents challenges in balancing knowledge retention and adaptation to new classes.
- Existing static and dynamic architectures in FSCIL face limitations such as overfitting or increased complexity.
- Selective State Space Models (SSMs) offer a promising avenue for dynamic adaptation without parameter space expansion.
Purpose of the Study:
- To explore the efficacy of Selective State Space Models (SSMs), specifically Mamba, for Few-Shot Class-Incremental Learning (FSCIL).
- To develop novel modules that leverage Mamba's capabilities for dynamic adaptation in FSCIL.
- To achieve state-of-the-art performance in FSCIL by preserving base-class knowledge while learning novel classes.
Main Methods:
- Proposed a dual selective SSM projector for input-conditioned, dynamic adaptation of state-space parameters.
- Implemented a frozen base branch for stable base-class feature preservation and a dynamic incremental branch for novel-class adaptation.
- Developed a class-sensitive selective scan mechanism to guide incremental learning and minimize disruption to base-class representations.
Main Results:
- Mamba-FSCIL demonstrated state-of-the-art performance on benchmark datasets including miniImageNet, CIFAR-100, and CUB-200.
- The dual SSM projector effectively decoupled base and novel class processing.
- The class-sensitive selective scan mechanism successfully reduced interference between base and novel classes.
Conclusions:
- Mamba-FSCIL effectively addresses the challenges of FSCIL by leveraging SSMs for dynamic, content-aware adaptation.
- The proposed dual projector and selective scan mechanism are key innovations for preserving knowledge and adapting to new classes.
- This work highlights the potential of Mamba-based architectures for advancing incremental learning paradigms.
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