Unsupervised Domain Adaptation for Human Pose Estimation

EasyChair Preprint 14870

Abstract

Human pose estimation (HPE) has seen significant advancements with the advent of deep learning and computer vision technologies. However, these advances often assume that the training and testing data come from the same distribution, which is rarely the case in real-world applications. The issue of domain shift—where the training and testing data differ significantly—poses a significant challenge for HPE systems. Unsupervised domain adaptation (UDA) offers a promising approach to address this challenge by leveraging unlabeled data from the target domain to improve model performance without requiring labeled examples from that domain. This article explores various unsupervised domain adaptation techniques applied to human pose estimation. By integrating these techniques into pose estimation models, we aim to improve accuracy and robustness in the face of domain shift. Our study evaluates several UDA methods, including feature alignment, adversarial learning, self-training, and domain-invariant representation learning, demonstrating their effectiveness through empirical results and comparative analysis. The findings highlight the potential of UDA to enhance pose estimation in diverse and challenging scenarios, providing insights into future directions for research and application.

Keyphrases: Model Robustness, Transfer Learning, adaptation techniques, computer vision, domain shift, human pose estimation, labeled data, machine learning, pose detection, unsupervised domain adaptation

@booklet{EasyChair:14870,
  author    = {Adeoye Ibrahim},
  title     = {Unsupervised Domain Adaptation for Human Pose Estimation},
  howpublished = {EasyChair Preprint 14870},
  year      = {EasyChair, 2024}}