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dataset

Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity

Zhou, Mu  
•
Stoffl, Lucas  
•
Mathis, Mackenzie  
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2023
Zenodo

Here we provide neural networks weights for the best models in our article "Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity", ICCV 2023. Each model has the naming convention "dataset"-"modeltype".pth. These pth files can be loaded with PyTorch. The code to load and use the models is available at: https://github.com/amathislab/BUCTD [Note: The weights for OCHuman, are called COCO-* as one only trains on COCO. So OCHuman-X := COCO-X]. We also share the predictions from various bottom-up models to reproduce the training stored in *.json format (compressed as zip files). See our repository for more details.

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Type
dataset
DOI
10.5281/zenodo.10039883
ACOUA ID

486d2de4-baf4-485b-bfb0-f8fd69d1f8c5

Author(s)
Zhou, Mu  
Stoffl, Lucas  
Mathis, Mackenzie  
Mathis, Alexander  
Date Issued

2023

Version

v4

Publisher

Zenodo

Subjects

COCO

•

CrowdPose

•

OCHuman

•

Pose estimation

•

ICCV 2023

RelationURL/DOI

IsNewVersionOf

https://zenodo.org/doi/10.5281/zenodo.8394791

IsSupplementTo

https://infoscience.epfl.ch/record/306250
Available on Infoscience
October 25, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/201910
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