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 )

Most implemented papers

Deep Residual Learning for Image Recognition

tensorflow/models 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 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 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 21 Mar 2017

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 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 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

PaddlePaddle/PaddleSpeech 28 May 2015

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 22 Mar 2017

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

facebookresearch/moco 9 Mar 2020

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 13 Jun 2020

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.