  In this paper, we studied the problem of localizing a generic set of
keypoints across multiple quadruped or four-legged animal species from images.
Due to the lack of large scale animal keypoint dataset with ground truth
annotations, we developed a novel dataset, AwA Pose, for keypoint detection of
quadruped animals from images. Our dataset contains significantly more
keypoints per animal and has much more diverse animals than the existing
datasets for animal keypoint detection. We benchmarked the dataset with a
state-of-the-art deep learning model for different keypoint detection tasks,
including both seen and unseen animal cases. Experimental results showed the
effectiveness of the dataset. We believe that this dataset will help the
computer vision community in the design and evaluation of improved models for
the generalized quadruped animal keypoint detection problem.
