Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning

• URL: http://arxiv.org/abs/2205.09995v1
• Date: Fri, 20 May 2022 07:25:33 GMT
• Title: Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning
• Authors: Yuzhong Chen, Zhenxiang Xiao, Lin Zhao, Lu Zhang, Haixing Dai, David Weizhong Liu, Zihao Wu, Changhe Li, Tuo Zhang, Changying Li, Dajiang Zhu, Tianming Liu, Xi Jiang
• Abstract summary: We propose a novel mask-guided vision transformer (MG-ViT) to achieve an effective and efficient few-shot learning on vision transformer (ViT) model. The MG-ViT model significantly improves the performance when compared with general fine-tuning based ViT models.
• Score: 10.29251906347605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances. In this paper, we propose a novel mask-guided vision transformer (MG-ViT) to achieve an effective and efficient FSL on ViT model. The key idea is to apply a mask on image patches to screen out the task-irrelevant ones and to guide the ViT to focus on task-relevant and discriminative patches during FSL. Particularly, MG-ViT only introduces an additional mask operation and a residual connection, enabling the inheritance of parameters from pre-trained ViT without any other cost. To optimally select representative few-shot samples, we also include an active learning based sample selection method to further improve the generalizability of MG-ViT based FSL. We evaluate the proposed MG-ViT on both Agri-ImageNet classification task and ACFR apple detection task with gradient-weighted class activation mapping (Grad-CAM) as the mask. The experimental results show that the MG-ViT model significantly improves the performance when compared with general fine-tuning based ViT models, providing novel insights and a concrete approach towards generalizing data-intensive and large-scale deep learning models for FSL.

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