Few-Shot Segmentation via Capturing Interclass and Intraclass Cues Using Class Activation Map

Abstract

Few-shot segmentation is a challenging task due to the limited class cues provided by a few of annotations. Discovering more class cues from known and unknown classes is the essential to few-shot segmentation. Existing method generates class cues mainly from common cues intra new classes where the similarity between support images and query images is measured to locate the foreground regions. However, the support images are not sufficient enough to measure the similarity since one or a few of support mask cannot describe the object of new class with large variations. In this paper, we capture the class cues by considering all images in the unknown classes, i.e., not only the support images but also the query images are used to capture the foreground regions. Moreover, the class-level labels in the known classes are also considered to capture the discriminative feature of new classes. The two aspects are achieved by class activation map which is used as attention map to improve the feature extraction. A new few-shot segmentation based on mask transferring and class activation map is proposed, and a new class activation map based on feature clustering is proposed to refine the class activation map. The proposed method is validated on Pascal Voc dataset. Experimental results demonstrate the effectiveness of the proposed method with larger mIoU values.