Boosting Few-Shot Continual Learning via Self-Adaptive Evolution

Few-shot continual learning (FSCL) has attracted increasing attention for real-world applications, where models must continuously adapt to new classes with only a few labeled samples while retaining prior knowledge. These abilities are essential in dynamic environments where data availability is often sparse and nonstationary. However, traditional FSCL methods are largely confined to closed data spaces, which limits their generalizability when diverse and evolving distributions are involved. Inspired by the paradigm of human lifelong learning, we propose a new self-adaptive evolution framework for FSCL that enables continuous interaction with and adaptation to external environments. To exploit latent knowledge in large-scale models, we use an adaptive diffusion-based generator that not only implicitly captures the distribution of new few-shot samples but also produces more high-quality samples. To mitigate the inevitable variability in generation quality, we also use a reinforced sample selection module, comprising a generated sample explorer and a selection evaluator, which explicitly guides the retained distributions toward alignment with the large-scale models. Integrated with the continual model, these components are optimized in an iterative self-adaptive evolution framework, ensuring stable knowledge retention while improving adaptability to newly emerging classes. We validate our approach through experiments on three benchmarks, revealing its effectiveness in exploiting external distributions and achieving notable performance improvements.