Co-Salient Object Detection via Discriminative Prototypes Contrast

Abstract

Co-salient object detection aims to detect co-salient objects in a group of images, combining collaborative segmentation and saliency detection, which is more challenging. Recent deep learning approaches identify co-salient objects by capturing the attention of consistent patterns within a group of images. However, due to limited semantic discriminability, these approaches often generate redundant attention unrelated to the co-salient object, resulting in inaccuracies. To address this, we refer to prototypical contrastive learning, and propose a prototype generation module to create discriminative prototypes representing both intra-group consistency and inter-group variance. These prototypes guide our proposed collaborative attention generation module, effectively enhancing co-salient object detection by highlighting relevant deep features. To ensure prototype's discriminability, we add contrastive supervision for multi-task learning. Additionally, we design a position-independent contrast loss function to enhance intra-group consistency representation. Experiments demonstrate the superiority of our approach over existing state-of-the-art approaches on three challenging benchmarks, i.e., CoCA,CoSOD3k,and CoSal2015.