PPNet(Part-aware Prototype Network for Few-shot Semantic Segmentation)[1] decompose the holistic class representation into a set of part-aware prototypes, and leverage unlabeled data to better modeling of intra-class variations. Besides, graph neural network model is used to generate and enhance the proposed part-aware prototypes. There are some details of reading and implementing it.

Contents

• Paper & Code & note
• Paper understanding
• Code understanding
• Note
• References

Paper & Code & note

Paper: Part-aware Prototype Network for Few-shot Semantic Segmentation
Code: PyTorch
Note: Mendeley

Paper

Abstract

Problem Description

Problem Solution

Conceptual Understanding

Core Conception

Part Generation

Part Refinement

Mask Generation

Experiments

Code

1. The complete code can be found in PPNet-PyTorch[2].

[Updating]

Note

• decomposed objects to a set of part based on prototype for few-shot segmentation.

References

[1] Liu Y, Zhang X, Zhang S, et al. Part-aware prototype network for few-shot semantic segmentation[C]//European Conference on Computer Vision. Springer, Cham, 2020: 142-158.
[2] PPNet-PyTorch. https://github.com/Xiangyi1996/PPNet-PyTorch.