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Test-Time Personalization with a Transformer for Human Pose Estimation

NeurIPS 2021 · Label-efficient and robust learning › Test-time adaptation · approach: Test-time adaptation of vision-language models · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add test-time personalization to Transformer keypoint trainingTransformer (keypoint) · Human 3.6MmPCK: 87.90 -> 91.70Helped
Add test-time personalization to Transformer keypoint trainingTransformer (keypoint) · Penn ActionmPCK: 86.16 -> 88.98Helped
Add test-time personalization to Transformer keypoint trainingTransformer (keypoint) · BBC PosemAcc: 90.19 -> 92.51Helped
Replace rotation prediction with self-supervised keypoint estimation (ablation)Feature-shared joint training · Human 3.6MmPCK: 87.37 -> 87.41Helped
Replace rotation prediction with self-supervised keypoint estimation (ablation)Feature-shared joint training · Penn ActionmPCK: 84.90 -> 85.78Helped
Replace rotation prediction with self-supervised keypoint estimation (ablation)Feature-shared joint training · BBC PosemAcc: 89.07 -> 89.65Helped
Add Transformer affinity mapping between self-supervised and supervised keypoints (ablation)Feature-shared keypoint model · Human 3.6MmPCK: 87.41 -> 87.90Helped
Add Transformer affinity mapping between self-supervised and supervised keypoints (ablation)Feature-shared keypoint model · Penn ActionmPCK: 85.78 -> 86.16Helped
Add Transformer affinity mapping between self-supervised and supervised keypoints (ablation)Feature-shared keypoint model · BBC PosemAcc: 89.65 -> 90.19Helped
Use offline instead of online personalization (ablation)Transformer (keypoint) · BBC PosemAcc: 92.51 -> 92.21Hurt
Use offline instead of online personalization (ablation)Transformer (keypoint) · Human 3.6MmPCK: 91.70 -> 92.05Helped
Use rotation prediction as the self-supervised objective (ablation)Feature-shared rotation model · Human 3.6MmPCK: 85.42 -> 84.90Hurt

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