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Research map / 3D vision / Human pose estimation

End-to-End Multi-Person Pose Estimation With Transformers

CVPR 2022 · 3D vision › Human pose estimation · approach: Multi-person pose estimation · 164 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add a joint decoder after the pose decoder to refine joint locations (ablation)PETR · COCO val2017AP: 66.4 -> 67.4Helped
Add pose and joint decoders instead of directly regressing poses from visual features (ablation)PETR · COCO val2017AP: 49.4 -> 67.4Helped
Add OKS loss (ablation)PETR with L1 loss and L1 matching · COCO val2017AP: 64.2 -> 65.6Helped
Use OKS for bipartite matching cost (ablation)PETR with L1 loss and L1 matching · COCO val2017AP: 64.2 -> 66.9Helped
Use both OKS loss and OKS matching (ablation)PETR · COCO val2017AP: 64.2 -> 67.4Helped
Use multi-scale testPETR with ResNet-50 · COCO test-devAP: 67.6 -> 69.2Helped
Use multi-scale testPETR with ResNet-101 · COCO test-devAP: 68.5 -> 70.0Helped
Use multi-scale testPETR with Swin-L · COCO test-devAP: 70.5 -> 71.2Helped
Use flipping testPETR · CrowdPose testAP: 71.6 -> 72.0Helped

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