Efficient Feature Reconstruction via <i>l</i> _2,1-Norm Regularization for Few-Shot Classification

Few-shot classification, which aims to recognize novel classes with the aid of learned knowledge and a few labeled samples, remains challenging and draws emerging concerns in computer vision. Metric-based methods incorporate the idea of metric learning into tackling the few-shot classification problem, i.e., the class membership of query samples can be determined in the latent space. Several recent methods propose to infer such memberships by feature reconstruction, which, however either exploit attention mechanisms or closed-form solutions. In this paper, we revisit the essence of feature reconstruction applied to few-shot classification and introduce a$l_{2,1}$-norm regularization to constraint it. As with the intention of attention mechanisms, we attempt to guide feature reconstruction towards focusing more on semantically rich target regions and diminishing the contribution of profitless features. We thereby obtain more discriminative query reconstructions for each class and perform classification based on these reconstruction errors. We formulate the proposal as a constrained optimization problem and design a simple and efficient iterative method as a solution. Furthermore, we provide the analysis on the convergence of proposed iterative method in theory. We conduct few-shot classification on two fine-grained and two general datasets. Extensive experimental results reveal that our method achieves new state-of-the-art performance.