Accurately measuring animal social bonds often relies on analyzing spatial proximity, yet advanced automated tracking methods have rarely been applied to feline interactions. To address this gap, this study utilized AI-based computer vision systems to extract precise distance data from video recordings of domestic cats engaging in social facial signaling. Contrary to initial predictions, the analysis revealed that cats maintained significantly closer physical proximity during non-affiliative encounters than during friendly, affiliative interactions. Additionally, the sex of the interacting pairs strongly influenced their spacing, with female-female dyads staying the closest across all contexts. Ultimately, this work demonstrates how machine learning tools can uncover nuanced behavioral strategies that animals use to navigate social environments.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection
A major challenge in training accurate facial landmark detection models
