Pseudo-Interacting Guided Network for Few-Shot Segmentation

Few-shot segmentation has got a lot of concerns recently. Existing methods mainly locate and recognize the target object based on a cross-guided way that applies masked target object features of support(query) images to make a feature matching with query(support) images. However, there are some differences between support images and query images because of large appearance and scale variation, which will lead to inaccurate and incomplete segmentation. This problem inspired us to explore the local coherence of the image to guide the segmentation. We try to get some target pixels in the query image and apply these pixels to search for more target pixels in the query image. In this work, we propose a novel network that combines a universal cross-guided branch with a new pseudo-interacting guided branch. Specifically, we first employ the universal cross-guided branch to produce a pseudo-labeling that represents the probability of each pixel belonging to the target object. Then we design a pseudo-interacting guided branch, which applies some pixels with high probabilities based on generated pseudo-labeling to segment the target object in the query image and revises the results of the cross-guided branch simultaneously. Extensive experiments show that our approach outperforms state-of-the-art methods on both PASCAL-5(i) and COCO-20(i) datasets.