Swin-Pose: Swin Transformer Based Human Pose Estimation
This work addresses the problem of human pose estimation for computer vision applications, offering an incremental improvement by applying transformer architecture to this domain.
The paper tackled human pose estimation by proposing Swin-Pose, a model based on Swin Transformer enhanced with a feature pyramid fusion structure, which achieved better performance compared to state-of-the-art CNN-based models.
Convolutional neural networks (CNNs) have been widely utilized in many computer vision tasks. However, CNNs have a fixed reception field and lack the ability of long-range perception, which is crucial to human pose estimation. Due to its capability to capture long-range dependencies between pixels, transformer architecture has been adopted to computer vision applications recently and is proven to be a highly effective architecture. We are interested in exploring its capability in human pose estimation, and thus propose a novel model based on transformer architecture, enhanced with a feature pyramid fusion structure. More specifically, we use pre-trained Swin Transformer as our backbone and extract features from input images, we leverage a feature pyramid structure to extract feature maps from different stages. By fusing the features together, our model predicts the keypoint heatmap. The experiment results of our study have demonstrated that the proposed transformer-based model can achieve better performance compared to the state-of-the-art CNN-based models.