SCL

Introduction

Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

We evaluated our approach using two baselines:

CANet [1] (project is here) and PFENet [2] (project is here).

Many thanks for their public project.

We followed the same setting with them.

You can follow the preparations of these two baselines, or you can find the running details in our documents.

References

[1] Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, and Chunhua Shen. Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5217–5226, 2019.

[2] Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia. Prior guided feature enrichment network for few-shot segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.