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Person Re-Identification

122 papers with code ยท Computer Vision

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 )

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Latest papers without code

When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey

29 Mar 2020

When autonomous systems consider the performance of accuracy and transferability simultaneously, several AI methods, like adversarial learning, reinforcement learning (RL) and meta-learning, show their powerful performance.

DEBLURRING DECISION MAKING DEPTH ESTIMATION IMAGE SUPER-RESOLUTION META-LEARNING PERSON RE-IDENTIFICATION RAIN REMOVAL ROBOT NAVIGATION STYLE TRANSFER TRANSFER LEARNING

Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification

27 Mar 2020

In this paper, we propose an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation.

VIDEO-BASED PERSON RE-IDENTIFICATION

Triplet Permutation Method for Deep Learning of Single-Shot Person Re-Identification

18 Mar 2020

Solving Single-Shot Person Re-Identification (Re-Id) by training Deep Convolutional Neural Networks is a daunting challenge, due to the lack of training data, since only two images per person are available.

PERSON RE-IDENTIFICATION

High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification

18 Mar 2020

When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score.

GRAPH MATCHING PERSON RE-IDENTIFICATION

Multi-task Learning with Coarse Priors for Robust Part-aware Person Re-identification

18 Mar 2020

MPN has three key advantages: 1) it does not need to conduct body part detection in the inference stage; 2) its model is very compact and efficient for both training and testing; 3) in the training stage, it requires only coarse priors of body part locations, which are easy to obtain.

MULTI-TASK LEARNING PERSON RE-IDENTIFICATION

Learning Shape Representations for Clothing Variations in Person Re-Identification

16 Mar 2020

To tackle the re-ID problem in the context of clothing changes, we propose a novel representation learning model which is able to generate a body shape feature representation without being affected by clothing color or patterns.

PERSON RE-IDENTIFICATION REPRESENTATION LEARNING

Structured Domain Adaptation for Unsupervised Person Re-identification

14 Mar 2020

The structured domain-translation network can effectively transform the source-domain images into the target domain while well preserving the original intra- and inter-identity relations.

UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED PERSON RE-IDENTIFICATION

When Person Re-identification Meets Changing Clothes

9 Mar 2020

We find that changing clothes makes Reid a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous Reid models to identify persons with unseen (new) clothes.

PERSON RE-IDENTIFICATION PERSON SEARCH