GSS: Graph-Based Subspace Learning with Shots Initialization for few-shot Recognition

Rui-Qi Wang, Xu-Yao Zhang, Cheng-Lin Liu

Abstract: Previous methods of few-shot Learning mostly solve different few-shot recognition tasks in an identical feature space. But identical features are hard to fit various tasks. Some works show that learning a unique subspace for each few-shot recognition task can improve the signal-noise ratio (SNR) of the features and boost the performance. However, there are still two problems remaining. First, in constructing the subspace for few-shot task, often some information (embeddings of queries or labels of shots) are discarded. Second, the eigendecomposition of covariance matrix is usually needed, which degrades the efficiency of the whole model. In this paper, we propose Graph-based Subspace learning with Shots initialization (GSS) for few-shot recognition to learn a better subspace efficiently. In GSS, the bases of the subspace are directly initialized with labels based on shots (given labeled samples) and iteratively updated for better discrimination based on a graph that connects bases and all samples. Extensive experiments on four few-shot benchmark datasets show that GSS reports better performance and higher efficient compared with previous subspace based methods and achieves state-of-the-art performance.