Assessing pain in horses is challenging because they instinctively hide discomfort, but shifts in their daily behavioral routines can provide vital clues. To bypass the impracticality of continuous manual observation, this study developed an automated video analysis pipeline using Large Vision Language Models (LVLMs). Researchers applied these models to stall surveillance videos of hospitalized horses to track time spent on activities like resting, feeding, and moving. These time budgets were then fed into machine learning classifiers to distinguish between painful and non-painful states. The system achieved an 80% accuracy in pain detection, proving that LVLMs offer a scalable, non-invasive solution for equine welfare monitoring without requiring expensive manual data annotation.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection
A major challenge in training accurate facial landmark detection models
