Summary of Towards Multi-modal Animal Pose Estimation: a Survey and In-depth Analysis, by Qianyi Deng et al.
Towards Multi-Modal Animal Pose Estimation: A Survey and In-Depth Analysis
by Qianyi Deng, Oishi Deb, Amir Patel, Christian Rupprecht, Philip Torr, Niki Trigoni, Andrew Markham
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents a comprehensive analysis of animal pose estimation (APE) methods, categorizing 176 papers since 2011 based on input sensors and modalities, output forms, learning paradigms, experimental setups, and application domains. The authors highlight current trends, challenges, and future directions in single- and multi-modality APE systems, including the transition from human to animal pose estimation. They also provide 2D and 3D APE datasets and evaluation metrics for different sensors and modalities. By studying APE methods, researchers can gain insights into the broader machine learning paradigm and reciprocally enrich human pose estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can understand animal body language. It’s like trying to figure out what a dog is thinking by looking at its ears and tail. The paper analyzes many different ways that researchers have tried to do this, using things like cameras, sensors, and even sound waves. They found some patterns and trends in these methods, which could help us better understand animals and even improve how we study human movement. |
Keywords
» Artificial intelligence » Machine learning » Pose estimation
