Few-shot segmentation based on high-resolution representation and Brownian distance covariance learning

The purpose of the few-shot segmentation task is to segment images containing new categories using only a few labeled samples. Existing methods typically extract features for support and query branches through a Siamese encoder and learn category-related information from the labeled support image to guide the segmentation of the query image. However, the extracted features are usually low- or middle-resolution, and these methods only exploit marginal distributions and neglect joint distributions. To address these issues, a High-Resolution representation and Brownian distance covariance learning (HRB) method is proposed for the task. Firstly, a high-resolution Siamese encoder is adopted to extract high-to-low-resolution features for two branches. Then, a pyramid feature joint module is proposed to learn high-resolution feature representations, and meanwhile, a new cross-excitation module is designed to enhance their common semantic information. Furthermore, a new similarity metric module is developed, i.e., the Brownian distance covariance metric, which estimates the semantic mapping relationship between the joint distribution features of the two branches. Extensive experiments on three benchmark datasets (PASCAL-5ⁱ, FSS-1000, and COCO-20ⁱ) prove that the proposed HRB attains state-of-the-art performance. The code is available at https://github.com/Saralyliu/HRB.