Papers › PE-former: Pose Estimation Transformer
PE-former: Pose Estimation Transformer
Paschalis Panteleris, Antonis Argyros
Vision transformer architectures have been demonstrated to work very effectively for image classification tasks. Efforts to solve more challenging vision tasks with transformers rely on convolutional backbones for feature extraction. In this paper we investigate the use of a pure transformer architecture (i.e., one with no CNN backbone) for the problem of 2D body pose estimation. We evaluate two ViT architectures on the COCO dataset. We demonstrate that using an encoder-decoder transformer architecture yields state of the art results on this estimation problem.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Pose Estimation | COCO (Common Objects in Context) | PEFORMER-Xcit-dino-p8 | AP | 72.6 | #9 of 10 | Archive leaderboard | report |
| Pose Estimation | COCO (Common Objects in Context) | PEFORMER-Xcit-dino-p8 | AR | 79.4 | #9 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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