Deviation Wing Loss for High-Performance 2D Pose Estimation

Heatmap regression using deep neural networks has become the dominant approach in 2D pose estimation. Nonetheless, the intrinsic variability in the flexibility of distinct keypoints engenders discernible momentum deviation among them, leading to training biases. Moreover, previous methods indiscriminately treat all pixels in a heatmap, further exacerbating biases. Regrettably, conventional loss functions, including the Mean Squared Error (MSE) loss, fail to rectify this issue adequately. Consequently, the need arises to recalibrate weights to concentrate the loss’s impact on specific regions. To this end, we introduce a novel Deviation Wing (DW) loss function for high-performance 2D pose estimation, incorporating two improvement aspects. Firstly, we use Gaussian Momentum Deviation encoding craft deviation maps, leveraging momentum deviation as a source of prior knowledge to enhance supervision over keypoints characterized by substantial momentum deviation. Subsequently, we harness the Cosine Wing function to amplify the loss concerning minor errors residing within the keypoint region and supervise errors across diverse scales. Our comprehensive empirical exploration spans multiple datasets encompassing 2D human pose and hand pose estimation. The experimental results demonstrate the efficacy of our proposed loss function in enhancing heatmap regression performances.