Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity
Description
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
The code to load and use the models is available at: https://github.com/amathislab/BUCTD
We also share the predictions from various bottom-up models to reproduce the training (as zip files). See our repository for more details.
Link to the article: https://openaccess.thecvf.com/content/ICCV2023/papers/Zhou_Rethinking_Pose_Estimation_in_Crowds_Overcoming_the_Detection_Information_Bottleneck_ICCV_2023_paper.pdf
We also provide
Files
coco.zip
Files
(1.9 GB)
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md5:17b6323cae57357a1d34214d9f85532a
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255.4 MB | Download |
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