Quantification of behavior is critical in applications ranging from
neuroscience, veterinary medicine and animal conservation efforts. 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 a series of technical innovations that enable a new method,
collectively called SuperAnimal, to develop and deploy deep learning models
that require zero additional human labels and model training. SuperAnimal
allows video inference on over 45 species with only two global classes of
animal pose models. If the models need fine-tuning, we show SuperAnimal models
are 10$\times$ more data efficient and outperform prior transfer learning
approaches. Moreover, we provide a new video-adaptation method to perform
unsupervised refinement of videos, and we illustrate the utility of our model
in behavioral classification. Collectively, this presents a data-efficient,
plug-and-play solution for behavioral analysis.

SuperAnimal 是一种新的解决行为分析中 pose 估计问题的 plug-and-play 解决方案，使用深度学习技术自动提取关键点，无需人工标记，并可应用于超过 45 种物种。