Person Re-Identification
372 papers with code • 27 benchmarks • 50 datasets
Person re-identification is the task of associating images of the same person taken from different cameras or from the same camera in different occasions.
( Image credit: PRID2011 dataset )
Benchmarks
These leaderboards are used to track progress in Person Re-Identification
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Libraries
Use these libraries to find Person Re-Identification models and implementations
Market-1501
CUHK03
DukeMTMC-reID
MSMT17
MARS
VIPeR
CUHK-SYSU
PRW
DukeMTMC-VideoReID
ETH
Subtasks
Most implemented papers
Deep Residual Learning for Image Recognition
tensorflow/models
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Densely Connected Convolutional Networks
liuzhuang13/DenseNet
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CVPR 2017
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
tensorflow/models
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CVPR 2018
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
Simple Online and Realtime Tracking with a Deep Association Metric
nwojke/deep_sort
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Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
A Simple Framework for Contrastive Learning of Visual Representations
google-research/simclr
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ICML 2020
This paper presents SimCLR: a simple framework for contrastive learning of visual representations.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
charlesq34/pointnet2
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NeurIPS 2017
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Domain-Adversarial Training of Neural Networks
Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
In Defense of the Triplet Loss for Person Re-Identification
layumi/Person_reID_baseline_pytorch
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In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning.
Improved Baselines with Momentum Contrastive Learning
Contrastive unsupervised learning has recently shown encouraging progress, e. g., in Momentum Contrast (MoCo) and SimCLR.
Bootstrap your own latent: A new approach to self-supervised Learning
deepmind/deepmind-research
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From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
