Refining Human Pose Detection Across Different Domains Without Source Data Access: Supplementary Content

EasyChair Preprint 14631

Abstract

Human pose detection is a critical component in various applications, including computer vision, robotics, and augmented reality. Traditional methods for pose estimation rely heavily on large amounts of annotated source data, which can be challenging to acquire, especially in diverse or unseen domains. This article explores novel approaches to refine human pose detection systems across different domains without the need for direct access to source data. By leveraging advanced techniques such as domain adaptation, synthetic data generation, and transfer learning, we aim to enhance the performance and generalizability of pose detection models. The proposed methods are evaluated using multiple benchmark datasets, demonstrating their effectiveness in improving pose estimation accuracy and robustness. This supplementary content provides a comprehensive overview of the methodologies employed, evaluation strategies, and results obtained, offering insights into advancing human pose detection technologies in data-scarce scenarios.

Keyphrases: Benchmark Datasets, Convolutional Neural Networks (CNNs), Domain Adaptation, Human Pose Detection, Synthetic Data Generation, Transfer Learning, computer vision, data scarcity, model generalization, pose estimation

@booklet{EasyChair:14631,
  author    = {Adeoye Ibrahim},
  title     = {Refining Human Pose Detection Across Different Domains Without Source Data Access: Supplementary Content},
  howpublished = {EasyChair Preprint 14631},
  year      = {EasyChair, 2024}}