ReID-Survey

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ReID-Survey is an Open Source research and education tool that provides resources and implementations for person re-identification using deep learning. It includes surveys on Transformer-based ReID and a powerful AGW baseline.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is ReID-Survey?

ReID-Survey is an open-source GitHub repository dedicated to deep learning for person re-identification. It offers comprehensive surveys, including an in-depth analysis of Transformer's impact across various Re-ID directions and a survey on deep learning for person re-identification with a powerful AGW baseline. The repository provides implementations for unsupervised Re-ID, cross-modality visible-infrared unsupervised Re-ID, and a unified experimental standard for animal Re-ID. Researchers can find code, datasets, and detailed experimental results for various Re-ID tasks, making it a valuable resource for advancing research in this field.

Best used for

Ideal for professors and researchers who need to explore and implement deep learning models for person re-identification, analyze Transformer's impact, and evaluate performance on various datasets. Especially valuable for those working on image/video-based, limited data, cross-modal, and special scenario Re-ID tasks.

Common actions

conduct research

implement deep learning models

evaluate re-identification techniques

access research resources

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Capabilities

Key features

Transformer-based ReID survey
AGW baseline implementation
Unsupervised Re-ID
Cross-modality Re-ID
Animal Re-ID standard
Experimental results

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of Re-identification tasks does ReID-Survey cover?

ReID-Survey covers a wide range of re-identification tasks, including single-modality image-based Re-ID, video-based Re-ID, partial Re-ID, cross-modality visible-infrared Re-ID, and even animal Re-ID. It provides resources and implementations for both supervised and unsupervised approaches.

Does ReID-Survey provide code implementations for the surveyed methods?

Yes, ReID-Survey provides code implementations for key methods like the AGW baseline and the Transformer-based UntransReID. These implementations are available for various Re-ID tasks, allowing researchers to reproduce results and build upon existing work.

What are the main contributions of the surveys included in ReID-Survey?

The surveys offer in-depth analysis of Transformer's impact on Re-ID across different directions and a comprehensive overview of deep learning for person Re-ID from 2016-2020. They also introduce new evaluation metrics like mINP and propose new baselines like AGW and UntransReID.

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