  One-shot semantic image segmentation aims to segment the object regions for
the novel class with only one annotated image. Recent works adopt the episodic
training strategy to mimic the expected situation at testing time. However,
these existing approaches simulate the test conditions too strictly during the
training process, and thus cannot make full use of the given label information.
Besides, these approaches mainly focus on the foreground-background target
class segmentation setting. They only utilize binary mask labels for training.
In this paper, we propose to leverage the multi-class label information during
the episodic training. It will encourage the network to generate more
semantically meaningful features for each category. After integrating the
target class cues into the query features, we then propose a pyramid feature
fusion module to mine the fused features for the final classifier. Furthermore,
to take more advantage of the support image-mask pair, we propose a
self-prototype guidance branch to support image segmentation. It can constrain
the network for generating more compact features and a robust prototype for
each semantic class. For inference, we propose a fused prototype guidance
branch for the segmentation of the query image. Specifically, we leverage the
prediction of the query image to extract the pseudo-prototype and combine it
with the initial prototype. Then we utilize the fused prototype to guide the
final segmentation of the query image. Extensive experiments demonstrate the
superiority of our proposed approach.

Computer Vision and Pattern Recognition;Multimedia