PE-former: Pose Estimation Transformer
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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Datasets
MS COCO
Methods
Absolute Position Encodings •
Adam •
Batch Normalization •
BPE •
Convolution •
Cross-Covariance Attention •
Dense Connections •
Depthwise Convolution •
Detr •
Dropout •
Feedforward Network •
GELU •
Label Smoothing •
Layer Normalization •
Linear Layer •
Local Patch Interaction •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Transformer •
XCiT •
XCiT Layer
