Summary of The Influence Of Faulty Labels in Data Sets on Human Pose Estimation, by Arnold Schwarz et al.
The Influence of Faulty Labels in Data Sets on Human Pose Estimation
by Arnold Schwarz, Levente Hernadi, Felix Bießmann, Kristian Hildebrand
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary: This study investigates how training data quality affects model performance in Human Pose Estimation (HPE), revealing that inaccurate labels in widely used datasets can negatively impact learning and performance metrics. The authors analyze popular HPE datasets to quantify the extent of label inaccuracies, showing that accounting for these issues will lead to more robust and accurate models for various real-world applications. Specifically, they demonstrate improved performance when using cleansed data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This study looks at how good or bad the training data is in a certain type of artificial intelligence called Human Pose Estimation (HPE). They found that if the training data has mistakes or incorrect information, it can make the AI model perform poorly. The researchers checked some popular datasets and discovered many errors. By fixing these mistakes, they were able to create more accurate and reliable models for real-life uses. |
Keywords
» Artificial intelligence » Pose estimation
