Generalized Few-Shot Semantic Segmentation

Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot Segmentation (FS-Seg) tackles this problem with many constraints. In this paper, we introduce a new benchmark, called Generalized Few-Shot Semantic Segmentation (GFS-Seg), to analyze the generalization ability of segmentation models to simultaneously recognize novel categories with very few examples as well as base categories with sufficient examples. Previous state-of-the-art FS-Seg methods fall short in GFS-Seg and the performance discrepancy mainly comes from the constrained training setting of FS-Seg. To make GFS-Seg tractable, we set up a GFS-Seg baseline that achieves decent performance without structural change on the original model. Then, as context is the key for boosting performance on semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by leveraging the contextual information to update class prototypes with aligned features. Extensive experiments on Pascal-VOC and COCO manifest the effectiveness of CAPL, and CAPL also generalizes well to FS-Seg.