Person reidentification

=== KaiyangZhou ===
https://github.com/KaiyangZhou/deep-person-reid datasets x 10 
https://github.com/yuminsuh/part_bilinear_reid fork of https://github.com/Cysu/open-reid 
https://github.com/wangguanan/light-reid derived from fast reid https://github.com/JDAI-CV/fast-reid (gitter.im fast-reid)
https://github.com/SY-Xuan/IICS Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by measuring the feature similarity without considering the distribution discrepancy among cameras, leading to degraded accuracy in label computation across cameras. This paper targets to address this challenge by studying a novel intra-inter camera similarity for pseudo-label generation. We decompose the sample similarity computation into two stage, i.e., the intra-camera and inter-camera computations, respectively. The intra-camera computation directly leverages the CNN features for similarity computation within each camera. Pseudo-labels generated on different cameras train the re-id model in a multi-branch network. The second stage considers the classification scores of each sample on different cameras as a new feature vector. This new feature effectively alleviates the distribution discrepancy among cameras and generates more reliable pseudo-labels. We hence train our re-id model in two stages with intra-camera and inter-camera pseudo-labels, respectively. This simple intra-inter camera similarity produces surprisingly good performance on multiple datasets, e.g., achieves rank-1 accuracy of 89.5% on the Market1501 dataset, outperforming the recent unsupervised works by 9+%, and is comparable with the latest transfer learning works that leverage extra annotations. 
https://github.com/qychen13/ClusterAlignReID
=== vimargu ===
https://github.com/vimar-gu/Bias-Eliminate-DA-ReID based on https://github.com/michuanhaohao/reid-strong-baseline
=== conference ===
https://www.youtube.com/watch?v=-ljItl_arX8 supervised and unsupervised learning.
=== layumi ===
https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial
=== Liang Zheng ===
http://www.liangzheng.com.cn/
https://github.com/layumi
https://github.com/zhunzhong07/IDE-baseline-Market-1501 
==== datasets ====
https://github.com/Yu-Wu/One-Example-Person-ReID used by Zheng repos. 
https://github.com/Yu-Wu/DukeMTMC-VideoReID
vision.cs.duke.edu/DukeMTMC/
=== Open reid ===
Open-ReID is a lightweight library of person re-identification for research purpose. It aims to provide a uniform interface for different datasets, a full set of models and evaluation metrics, as well as examples to reproduce (near) state-of-the-art results.
https://github.com/Cysu/open-reid
=== Yutian Lin ===
I'm Yutian Lin, a third year PhD student , under the supervision Dr. Liang Zheng. 
Focus is on person re-ID and related applications. 
https://vana77.github.io/ , https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification ,, https://github.com/Yu-Wu/Exploit-Unknown-Gradually
https://github.com/vana77/DukeMTMC-attribute
=== links ===
Deep sort person tracking
https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification
Category:Neural networks