Contrastive enhancement using latent prototype for few-shot segmentation

Abstract

Few-shot segmentation is a task that aims to generalize well to segment novel categories in images leveraging only a few annotated samples. Most existing methods adopt the prototype learning architecture, where support prototype vectors are expanded and concatenated with query features to perform conditional segmentation. However, such framework may potentially focus more on query features and neglect the similarity between support and query features. Moreover, it is valuable to mine information and latent categories in the background region to improve method generalization. To address these issues, this paper proposes a contrastive enhancement approach that can mine latent prototypes from background regions and leverage latent classes to raise the utilization of similarity information between prototype and query features. Specifically, a latent prototype sampling module is proposed to generate pseudo-mask and novel prototypes from the region with same category. It is based on feature similarity in high-level features, which is cost-effective for conducting end-to-end learning. This module does not require prior knowledge of object categories and has no strong dependence on clustering numbers like cluster-based method. Besides, a contrastive enhancement module is developed to drive models to provide different predictions with the same query features. It introduces the latent prototypes to regularize the decoder to concentrate more on similarity between support and query features. The proposed modules can be used as auxiliary modules to flexibly integrate into other baselines for better segmentation performance. Extensive experiments show our approach remarkably improves the performance of state-of-the-art methods for 1-shot and 5-shot segmentation, especially outperforming baseline by 5.9% and 7.3% for 5-shot task on Pascal-5i and COCO-20i.