Attention-Based Multi-View Feature Collaboration for Decoupled Few-Shot Learning

Abstract

Decoupled Few-shot learning (FSL) is an effective methodology that deals with the problem of data-scarce. Its standard paradigm includes two phases: (1) Pre-train. Generating a CNN-based feature extraction model (FEM) via base data. (2) Meta-test. Employing the frozen FEM to obtain the novel data features, then classifying them. Obviously, one crucial factor, the category gap, prevents the development of FSL, i.e., it is challenging for the pre-trained FEM to adapt to the novel class flawlessly. Inspired by a common-sense theory: the FEMs based on different strategies focus on different priorities, we attempt to address this problem from the multi-view feature collaboration (MVFC) perspective. Specifically, we first denoise the multi-view features by subspace learning method, then design three attention blocks (loss-attention block, self-attention block and graph-attention block) to balance the representation between different views. The proposed method is evaluated on four benchmark datasets and achieves significant improvements of 0.9%-5.6% compared with SOTAs.