  Timely detection of horse pain is important for equine welfare. Horses
express pain through their facial and body behavior, but may hide signs of pain
from unfamiliar human observers. In addition, collecting visual data with
detailed annotation of horse behavior and pain state is both cumbersome and not
scalable. Consequently, a pragmatic equine pain classification system would use
video of the unobserved horse and weak labels. This paper proposes such a
method for equine pain classification by using multi-view surveillance video
footage of unobserved horses with induced orthopaedic pain, with temporally
sparse video level pain labels. To ensure that pain is learned from horse body
language alone, we first train a self-supervised generative model to
disentangle horse pose from its appearance and background before using the
disentangled horse pose latent representation for pain classification. To make
best use of the pain labels, we develop a novel loss that formulates pain
classification as a multi-instance learning problem. Our method achieves pain
classification accuracy better than human expert performance with 60% accuracy.
The learned latent horse pose representation is shown to be viewpoint
covariant, and disentangled from horse appearance. Qualitative analysis of pain
classified segments shows correspondence between the pain symptoms identified
by our model, and equine pain scales used in veterinary practice.
