Multi-scale task-aware structure graph modeling for few-shot image recognition

The Few-shot image recognition attempts to recognize images from a novel class with only a limited number of labeled training images, which is a few-shot learning (FSL) task. FSL is very challenging. Limited labeled training samples cannot adequately represent the distribution of classes, and the base and novel classes in the training and testing stages do not intersect and have different distributions, leading to a domain shift problem in generalizing the learned model to the novel class dataset. In this paper, we propose multi-scale task-aware structure graph modeling for few-shot image recognition. We train a meta-filter learner to generate task-aware local structure filters for each scale and adaptively capture the local structures at each scale. Moreover, we introduce a novel multi-scale graph attention network (MGAT) module to model the multi-scale local structures of an image, fully exploring the correlations between different local structures at all scales of the image. Finally, we leverage the attention mechanism of graph attention network to achieve information aggregation and propagation, aiming to obtain more representative and discriminative local structure features that integrate both local and global information. We conducted comprehensive experiments on four benchmark datasets widely adopted in FSL tasks. The experimental results demonstrate that the MTSGM obtains state-of-the-art performance, which validates the effectiveness of MTSGM.