Multi-stage fusion of local and global features for few-shot image classification

Few-shot classification is a very challenging task of computer vision. Recently, different from meta-learning, transfer-learning foregoing the episodic training strategy has gradually become popular in this community. Under this pipeline, how to learn a high-quality feature representation is vital for winning good performance. However, current works mainly build the classification model upon convolutional neural networks, which cannot extract discriminative features. To address the above problem, we propose exploring the non-local networks to construct classification model, which is trained by the joint learning of supervised and self-supervised tasks to obtain global invariant features. Further, we propose a few-shot classification algorithm using multi-stage fusion of local and global features, in which the fusion of features happens simultaneously during two stages of transfer-learning. The stage of pre-training implements parallel mechanism, in which the local feature network and global feature network mutually learn from each other, while the stage of few-shot testing implements serial mechanism through feature concatenation. We conducted extensive evaluations on multiple benchmark datasets to demonstrate the effectiveness of our method. Ablation studies have shown the effectiveness of the multi-stage feature fusion, and the comparison results have shown that our method can achieve better performance compared with other state-of-the-art methods.