Enhancing Few-Shot Classification without Forgetting through Multi-Level Contrastive Constraints
<|reference_start|>Enhancing Few-Shot Classification without Forgetting through Multi-Level Contrastive Constraints: Most recent few-shot learning approaches are based on meta-learning with episodic training. However, prior studies encounter two crucial problems: (1) \textit{the presence of inductive bias}, and (2) \textit{the occurrence of catastrophic forgetting}. In this paper, we propose a novel Multi-Level Contrastive Constraints (MLCC) framework, that jointly integrates within-episode learning and across-episode learning into a unified interactive learning paradigm to solve these issues. Specifically, we employ a space-aware interaction modeling scheme to explore the correct inductive paradigms for each class between within-episode similarity/dis-similarity distributions. Additionally, with the aim of better utilizing former prior knowledge, a cross-stage distribution adaption strategy is designed to align the across-episode distributions from different time stages, thus reducing the semantic gap between existing and past prediction distribution. Extensive experiments on multiple few-shot datasets demonstrate the consistent superiority of MLCC approach over the existing state-of-the-art baselines.<|reference_end|>
@article{chen2024enhancing,
    title={Enhancing Few-Shot Classification without Forgetting through Multi-Level
  Contrastive Constraints},
    author={Bingzhi Chen, Haoming Zhou, Yishu Liu, Biqing Zeng, Jiahui Pan and
  Guangming Lu},
    journal={arXiv preprint arXiv:2409.11286},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11286},
    primaryClass={cs.MM}
}