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Models/BUCTD (CID-W32)

BUCTD (CID-W32)

Reported on 6 benchmarks across 3 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision2 results

Pose EstimationonOCHuman
Test AP· 2023-06-13
47.2
best: 93.3 (ViTPose (ViTAE-G, GT bounding boxes))
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity arXiv:2306.07879
Pose EstimationonOCHuman
Validation AP· 2023-06-13
47.7
best: 92.8 (ViTPose (ViTAE-G, GT bounding boxes))
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity arXiv:2306.07879

Methodology2 results

3DonOCHuman
Test AP· 2023-06-13
47.2
best: 93.3 (ViTPose (ViTAE-G, GT bounding boxes))
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity arXiv:2306.07879
3DonOCHuman
Validation AP· 2023-06-13
47.7
best: 92.8 (ViTPose (ViTAE-G, GT bounding boxes))
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity arXiv:2306.07879

Audio2 results

1 Image, 2*2 StitchionOCHuman
Test AP· 2023-06-13
47.2
best: 93.3 (ViTPose (ViTAE-G, GT bounding boxes))
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity arXiv:2306.07879
1 Image, 2*2 StitchionOCHuman
Validation AP· 2023-06-13
47.7
best: 92.8 (ViTPose (ViTAE-G, GT bounding boxes))
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity arXiv:2306.07879