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 simultaneously segmenting the novel categories with very few examples and the base categories with sufficient examples. It is the first study showing that previous representative state-of-the-art FS-Seg methods fall short in GFS-Seg and the performance discrepancy mainly comes from the constrained 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, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image. Both two contributions are experimentally manifested for their substantial practical merit. Extensive experiments on Pascal-Voc and COCO also show that CAPL generalizes well to FS-Seg by achieving competitive performance. Code is available at https://github.com/dvlab-research/GFS-Seg.

1. Introduction

Development of deep learning has yielded significant performance gain to semantic segmentation tasks. Representative semantic segmentation methods [5], [64] benefit a wide range of applications for robotics, automatic driving, medical imaging, etc. However, once these frameworks are trained, without sufficient fully-labeled data, they are unable to deal with unseen classes in new applications. Even if the required data of novel classes are ready, fine-tuning costs additional time and resources.