MoTiC: Momentum Tightness and Contrast for Few-Shot Class-Incremental Learning

• URL: http://arxiv.org/abs/2509.19664v1
• Date: Wed, 24 Sep 2025 00:41:40 GMT
• Title: MoTiC: Momentum Tightness and Contrast for Few-Shot Class-Incremental Learning
• Authors: Zeyu He, Shuai Huang, Yuwu Lu, Ming Zhao,
• Abstract summary: Few-Shot Class-Incremental Learning (FSCIL) must contend with the dual challenge of learning new classes from scarce samples while preserving old class knowledge.<n>New-class prototypes suffer significant estimation bias due to extreme data scarcity, whereas base-class prototypes benefit from sufficient data.<n>We show that aligning the new-class priors with old-class statistics via Bayesian analysis reduces variance and improves prototype accuracy.
• Score: 20.22260611239967
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Few-Shot Class-Incremental Learning (FSCIL) must contend with the dual challenge of learning new classes from scarce samples while preserving old class knowledge. Existing methods use the frozen feature extractor and class-averaged prototypes to mitigate against catastrophic forgetting and overfitting. However, new-class prototypes suffer significant estimation bias due to extreme data scarcity, whereas base-class prototypes benefit from sufficient data. In this work, we theoretically demonstrate that aligning the new-class priors with old-class statistics via Bayesian analysis reduces variance and improves prototype accuracy. Furthermore, we propose large-scale contrastive learning to enforce cross-category feature tightness. To further enrich feature diversity and inject prior information for new-class prototypes, we integrate momentum self-supervision and virtual categories into the Momentum Tightness and Contrast framework (MoTiC), constructing a feature space with rich representations and enhanced interclass cohesion. Experiments on three FSCIL benchmarks produce state-of-the-art performances, particularly on the fine-grained task CUB-200, validating our method's ability to reduce estimation bias and improve incremental learning robustness.

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