Summary of Posebench: Benchmarking the Robustness Of Pose Estimation Models Under Corruptions, by Sihan Ma et al.
PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions
by Sihan Ma, Jing Zhang, Qiong Cao, Dacheng Tao
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces PoseBench, a comprehensive benchmark for evaluating the robustness of pose estimation models against real-world corruption. The goal is to assess the accuracy and safety of pose estimation models in practical scenarios. The authors evaluated 60 representative models across three datasets for human and animal pose estimation, considering 10 types of corruption in four categories: blur and noise, compression and color loss, severe lighting, and masks. The findings reveal that state-of-the-art models are vulnerable to common real-world corruptions and exhibit distinct behaviors when tackling human and animal pose estimation tasks. To improve model robustness, the authors explore various design considerations, including input resolution, pre-training datasets, backbone capacity, post-processing, and data augmentations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pose estimation is important for things like video games and self-driving cars. It’s a way to figure out where body parts are in pictures. But current models can get tricked by messy or old photos. This paper makes a special test to see how well different models do when the pictures are messed up. They tried 60 different models on three types of pictures and found that even the best models got it wrong sometimes. To make better models, they looked at things like making the pictures clearer and using more information from the photos. |
Keywords
» Artificial intelligence » Pose estimation
