2024-06-21 スイス連邦工科大学ローザンヌ校(EPFL)
<関連情報>
- https://actu.epfl.ch/news/unifying-behavioral-analysis-through-animal-founda/
- https://www.nature.com/articles/s41467-024-48792-2
行動解析のためのSuperAnimal事前学習済みポーズ推定モデル SuperAnimal pretrained pose estimation models for behavioral analysis
Shaokai Ye,Anastasiia Filippova,Jessy Lauer,Steffen Schneider,Maxime Vidal,Tian Qiu,Alexander Mathis & Mackenzie Weygandt Mathis
Nature Communications Published:21 June 2024
DOI:https://doi.org/10.1038/s41467-024-48792-2
Abstract
Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models. We present SuperAnimal, a method to develop unified foundation models that can be used on over 45 species, without additional manual labels. These models show excellent performance across six pose estimation benchmarks. We demonstrate how to fine-tune the models (if needed) on differently labeled data and provide tooling for unsupervised video adaptation to boost performance and decrease jitter across frames. If fine-tuned, SuperAnimal models are 10–100× more data efficient than prior transfer-learning-based approaches. We illustrate the utility of our models in behavioral classification and kinematic analysis. Collectively, we present a data-efficient solution for animal pose estimation.
