Publication

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

Name: Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity
Published: 2023

Alexander Mathis, Mackenzie Mathis, Mu Zhou, Lucas Maximilian Stoffl
2023
Datasets

Abstract

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.

Official source

https://infoscience.epfl.ch/entities/publication/8d945cd3-f6e9-4c84-b3ac-fab49cca3f9c

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