Recently, there has been a surge of interest in applying deep learning techniques to animal behavior recognition, particularly leveraging pre-trained visual language models, such as CLIP, due to their remarkable generalization capacity across various downstream tasks. However, adapting these models to the specific domain of animal behavior recognition presents two significant challenges: integrating motion information and devising an effective temporal modeling scheme. In this paper, we propose AnimalMotionCLIP to address these challenges by interleaving video frames and optical flow information in the CLIP framework. Additionally, several temporal modeling schemes using an aggregation of classifiers are proposed and compared: dense, semi dense, and sparse. As a result, fine temporal actions can be correctly recognized, which is of vital importance in animal behavior analysis. Experiments on the Animal Kingdom dataset demonstrate that AnimalMotionCLIP achieves superior performance compared to state-of-the-art approaches.
View on arXiv@article{zhong2025_2505.00569,
title={ AnimalMotionCLIP: Embedding motion in CLIP for Animal Behavior Analysis },
author={ Enmin Zhong and Carlos R. del-Blanco and Daniel Berjón and Fernando Jaureguizar and Narciso García },
journal={arXiv preprint arXiv:2505.00569},
year={ 2025 }
}