CANet : class-agnostic segmentation networks with iterative refinement and attentive few-shot learning

Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.AI SingaporeMinistry of Education (MOE)Accepted versionG. Lin’s participation was partly supported by the National Research Foundation Singapore under its AI Singapore Programme [AISG-RP-2018-003] and a MOE Tier-1 research grant [RG126/17 (S)]. R. Yao’s participation was supported by the National Natural Scientific Foundation of China (NSFC) under Grant No. 61772530. We would like to thank NVIDIA for GPU donation. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore