Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification

Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to accommodate new tasks, given only a few examples. To handle the limited-data in few-shot regimes, recent methods tend to collectively use a set of local features to densely represent an image instead of using a mixed global feature. They generally explore a unidirectional paradigm, e.g., finding the nearest support feature for every query feature and aggregating local matches for a joint classification. In this paper, we propose a novel Mutual Centralized Learning (MCL) to fully affiliate these two disjoint dense features sets in a bidirectional paradigm. We first associate each local feature with a particle that can bidirectionally random walk in discrete feature space. To estimate the class probability, we propose the dense features' accessibility that measures the expected number of visits to the dense features of that class in a Markov process. We relate our method to learning a centrality on an affiliation network and demonstrate its capability to be plugged in existing methods by highlighting centralized local features. Experiments show that our method achieves the new state-of-the-art.