The Influence of Faulty Labels in Data Sets on Human Pose Estimation

In this study we provide empirical evidence demonstrating that the quality of training data impacts model performance in Human Pose Estimation (HPE). Inaccurate labels in widely used data sets, ranging from minor errors to severe mislabeling, can negatively influence learning and distort performance...

Full description

Saved in:
Bibliographic Details
Main Authors Schwarz, Arnold, Hernadi, Levente, Bießmann, Felix, Hildebrand, Kristian
Format Journal Article
LanguageEnglish
Published 05.09.2024
Subjects
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Online AccessGet full text

Cover

More Information
Summary:In this study we provide empirical evidence demonstrating that the quality of training data impacts model performance in Human Pose Estimation (HPE). Inaccurate labels in widely used data sets, ranging from minor errors to severe mislabeling, can negatively influence learning and distort performance metrics. We perform an in-depth analysis of popular HPE data sets to show the extent and nature of label inaccuracies. Our findings suggest that accounting for the impact of faulty labels will facilitate the development of more robust and accurate HPE models for a variety of real-world applications. We show improved performance with cleansed data.
DOI:10.48550/arxiv.2409.03887