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.

There are the overall of toolkit for Deep Learning.
https://github.com/Gojay001/toolkit-DeepLearning

SORT[1] is pragmatic approach for online and realtime applications. It achieves SOTA with using Kalman filter and Hungarian algorithm. There are some details of reading and implementing it.