Consequently, a pragmatic equine pain classification system would use video of the unobserved horse and weak labels.
In this paper we present our preliminary work on model-based behavioral analysis of horse motion.
Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset.
We present a method for weakly-supervised action localization based on graph convolutions.
Instead of directly finetuning a network trained to detect keypoints on human faces to animal faces (which is sub-optimal since human and animal faces can look quite different), we propose to first adapt the animal images to the pre-trained human detection network by correcting for the differences in animal and human face shape.