The Influence of Faulty Labels in Data Sets on Human Pose Estimation
This addresses data quality issues for researchers and practitioners in computer vision, but it is incremental as it focuses on analyzing existing data sets rather than introducing new methods.
The study tackled the problem of faulty labels in human pose estimation data sets, finding that inaccurate labels negatively impact model performance and showing improved results with cleansed data.
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.