SuperAnimal models pretrained for plug-and-play analysis of animal behavior

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.

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