EfficientFSL: Enhancing Few-Shot Classification via Query-Only Tuning in Vision Transformers

• URL: http://arxiv.org/abs/2601.08499v2
• Date: Thu, 15 Jan 2026 02:50:30 GMT
• Title: EfficientFSL: Enhancing Few-Shot Classification via Query-Only Tuning in Vision Transformers
• Authors: Wenwen Liao, Hang Ruan, Jianbo Yu, Bing Song, YuansongWang, Xiaofeng Yang,
• Abstract summary: Large models such as Vision Transformers (ViTs) have demonstrated remarkable superiority over smaller architectures like ResNet in few-shot classification.<n>We propose EfficientFSL, a query-only fine-tuning framework tailored specifically for few-shot classification with ViT.<n>With minimal trainable parameters, EfficientFSL achieves state-of-the-art performance on four in-domain few-shot datasets and six cross-domain datasets.
• Score: 4.880377460177786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large models such as Vision Transformers (ViTs) have demonstrated remarkable superiority over smaller architectures like ResNet in few-shot classification, owing to their powerful representational capacity. However, fine-tuning such large models demands extensive GPU memory and prolonged training time, making them impractical for many real-world low-resource scenarios. To bridge this gap, we propose EfficientFSL, a query-only fine-tuning framework tailored specifically for few-shot classification with ViT, which achieves competitive performance while significantly reducing computational overhead. EfficientFSL fully leverages the knowledge embedded in the pre-trained model and its strong comprehension ability, achieving high classification accuracy with an extremely small number of tunable parameters. Specifically, we introduce a lightweight trainable Forward Block to synthesize task-specific queries that extract informative features from the intermediate representations of the pre-trained model in a query-only manner. We further propose a Combine Block to fuse multi-layer outputs, enhancing the depth and robustness of feature representations. Finally, a Support-Query Attention Block mitigates distribution shift by adjusting prototypes to align with the query set distribution. With minimal trainable parameters, EfficientFSL achieves state-of-the-art performance on four in-domain few-shot datasets and six cross-domain datasets, demonstrating its effectiveness in real-world applications.

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