Cross-Domain Adaptation for Animal Pose Estimation
This addresses the lack of labeled data for animal pose estimation, enabling applications in fields like biology and robotics, though it is incremental as it builds on existing adaptation techniques.
The paper tackles the problem of animal pose estimation by building a dataset and proposing a cross-domain adaptation method to transfer knowledge from labeled to unlabeled animal classes, achieving convincing results.
In this paper, we are interested in pose estimation of animals. Animals usually exhibit a wide range of variations on poses and there is no available animal pose dataset for training and testing. To address this problem, we build an animal pose dataset to facilitate training and evaluation. Considering the heavy labor needed to label dataset and it is impossible to label data for all concerned animal species, we, therefore, proposed a novel cross-domain adaptation method to transform the animal pose knowledge from labeled animal classes to unlabeled animal classes. We use the modest animal pose dataset to adapt learned knowledge to multiple animals species. Moreover, humans also share skeleton similarities with some animals (especially four-footed mammals). Therefore, the easily available human pose dataset, which is of a much larger scale than our labeled animal dataset, provides important prior knowledge to boost up the performance on animal pose estimation. Experiments show that our proposed method leverages these pieces of prior knowledge well and achieves convincing results on animal pose estimation.