Mixer-Based Semantic Spread for Few-Shot Learning
Jun Sheng Cheng, Fusheng Hao, Fengxiang He, Liu Liu, Qieshi Zhang · IEEE Transactions on Multimedia · 2021
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 amixermodule 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 themixerfor 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 goodmixer. Visualization results validate the feasibility of ourmixer. 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.