PE-former: Pose Estimation Transformer
This addresses pose estimation for computer vision applications, offering a novel approach by eliminating reliance on convolutional backbones.
The paper tackles 2D body pose estimation by using a pure transformer architecture without a CNN backbone, achieving state-of-the-art results on the COCO dataset.
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.