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.

CANet(CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning)[1] consists of a two-branch dense comparison module which performs multi-level feature comparison, and an iterative optimization module which iteratively refines the predicted results. There are some details of reading and implementing it.

SG-One(SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation)[1] adopt a masked average pooling strategy for producing the guidance features, then leverage the cosine similarity to build the relationship. There are some details of reading and implementing it.

co-FCN(Conditional Networks for Few-Shot Semantic Segmentation)[1] handle sparse pixel-wise annotations to achieve nearly the same accuracy. There are some details of reading and implementing it.

OSLSM(One-Shot Learning for Semantic Segmentation)[1] firstly proposed two-branch approach to one-shot semantic segmentation. Conditioning branch trains a network to get parameter $\theta$, and Segmentaion branch outputs the final mask based on parameter $\theta$. There are some details of reading and implementing it.

Mask R-CNN[1] is a framework for object instance segmentation, which adds a branch for predicting an object mask in parallel with the existing branch for bounding box recognition of Faster R-CNN. There are some details of reading and implementing it.

LTM(Local Transformation Module)[1] focus on the relationship of the local features. It uses linear transformation of the relationship matrix in a high-dimensional metric embedding space to accomplish the transformation. There are some details of reading and implementing it.

PGNet(Pyramid Graph Networks)[1] modeled structured segmentation data with graphs and further proposed a pyramid-like structure that models different sizes of image regions as graph nodes. There are some details of reading and implementing it.

CRNet(Cross-Reference Networks)[1] make predictions for both the support image and the query image. It can better find the co-occurrent objects in the two images, thus helping the few-shot segmentation task. There are some details of reading and implementing it.

FSL-Survey[1] is a survey on Few-Shot Learning(FSL), which cotains 166 paper to review Few-Shot Learning. They categorize FSL methods from three perspectives: data, model and algorithm. There are some details of reading it.