Mixer-Based Semantic Spread for Few-Shot Learning

Key semantics can come from everywhere on an image. Semantic alignment is a key part of few-shot learning but still remains challenging. In this paper, we design a Mixer-Based Semantic Spread (MBSS) algorithm that employs a {\it mixer} module to spread the key semantic on the whole image, so that one can directly compare the processed image pairs. We first adopt a convolutional neural network to extract features from both support and query images and separate each of them into multiple Local Descriptor-based Representations (LDRs). The LDRs are then fed into the {\it mixer} for semantic spread, where every LDR attracts complementary information from its peers. In this way, the objective semantic is made spread on the whole image in a data-driven manner. The overall pipeline is supervised by a voting-based loss, guaranteeing a good {\it mixer}. Visualization results validate the feasibility of our {\it mixer}. Comprehensive experiments on three benchmark datasets, miniImageNet, tieredImageNet, and CUB, show that our algorithm achieves the state-of-the-art performance in both $5$ -way $1$ -shot and $5$ -way $5$ -shot settings.