Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
This work addresses few-shot semantic segmentation for computer vision, offering a novel method to improve robustness to appearance variations, but it is incremental as it builds on existing feature matching approaches.
The paper tackles the problem of few-shot semantic segmentation, where appearance variations cause unreliable feature matching, by proposing a Support-induced Graph Convolutional Network (SiGCN) that explicitly excavates latent context structure in query images, achieving state-of-the-art performance on PASCAL-5i and COCO-20i benchmarks.
Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples and has made great progress recently. Most of the existing FSS models focus on the feature matching between support and query to tackle FSS. However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction. To this end, we propose a Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate latent context structure in query images. Specifically, we propose a Support-induced Graph Reasoning (SiGR) module to capture salient query object parts at different semantic levels with a Support-induced GCN. Furthermore, an instance association (IA) module is designed to capture high-order instance context from both support and query instances. By integrating the proposed two modules, SiGCN can learn rich query context representation, and thus being more robust to appearance variations. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our SiGCN achieves state-of-the-art performance.