CANet: Class-Agnostic Segmentation Networks with Iterative Refinement andAttentive Few-Shot Learning
Abstract
Recent progress in semantic segmentation is driven by
deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixelwise 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.