Target-aware for Few-shot Segmentation

Few-shot segmentation refers to learn a segmentation model that can be generalized to novel classes with limited labeled images. Establishing the correspondence between support images and query images effectively has a considerable effect on guiding the segmentation of query images. Most existing methods mainly adopt a trained classification network as the backbone, nevertheless, the classification tasks only focus on the most discriminate regions of the target rather than the targets' integrity and the most discriminate regions may not be part of the target we need to segment while multiple classes object included in images. Besides, there exists another question that the most discriminate regions of the target in support image also do not necessarily appear in query images because of occlusion or incomplete object. All these may cause the correspondence between two images inaccurately. To tackle these problems, we propose a Target-aware Network(TaNet). Our network has two objectives: (1) increasing both intra-object similarity and inter-object dissimilarity for query image and support image to make each object more complete rather than highlight the most discriminate regions; (2) adaptively generating target-aware correspondence between support images and query images. Experiments on PASCAL-5(i) and COCO-20(i) show that our method achieves state-of-the-art performance.