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.

PV-RCNN[1] is a 3D Object Detection framework to integrate 3D voxel CNN and PointNet-based set abstraction to learn more discriminative point cloud features. The most contributions in this papar is two-stage strategy including the voxel-to-keypoint 3D scene encoding and the keypoint-to-grid RoI feature abstraction. There are some details of reading and implementing it.

DeepSORT[1] integrates appearance information to improve the performance of SORT, learned a deep association metric. There are some details of reading and implementing it.