数据标签错误会严重拖累人体姿态估计模型性能。
The Influence of Faulty Labels in Data Sets on Human Pose Estimation
- 分析主流数据集中的标签错误类型与程度
- 清洗数据后模型性能显著提升
- 适合关注真实场景下模型鲁棒性的研究者
本研究通过实证表明，训练数据质量直接影响人体姿态估计（HPE）模型性能。广泛使用的数据集中存在从轻微误差到严重误标的各种标签问题，这些错误会干扰模型学习并扭曲性能评估结果。我们对多个主流HPE数据集进行了深入分析，揭示了标签不准确的范围与性质。研究发现，考虑标签错误的影响有助于开发更鲁棒、更精准的HPE模型，适用于多种实际应用场景。使用清洗后的数据可获得明显性能提升。
原文摘要 · Abstract (English)
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.
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