PANet(PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment)[1] learns class-specific prototype representations for images and matches each pixel to the learned prototypes. There are some details of reading and implementing it.

Contents

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

Paper & Code & note

Paper: PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment(ICCV 2019 paper)
Code: PyTorch
Note: Mendeley

Paper

Abstract

Problem Solution

Conceptual Understanding

Prototype learning

Non-parametric metric learning

Core Conception

Experiments

Code

[Updating]

Note

• provided a baseline in prototype learning for few-shot segmentation.

References

[1] Wang K, Liew J H, Zou Y, et al. Panet: Few-shot image semantic segmentation with prototype alignment[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 9197-9206.
[2] PANet. https://github.com/kaixin96/PANet.