  Neural networks are highly effective tools for pose estimation. However, as
in other computer vision tasks, robustness to out-of-domain data remains a
challenge, especially for small training sets that are common for real-world
applications. Here, we probe the generalization ability with three architecture
classes (MobileNetV2s, ResNets, and EfficientNets) for pose estimation. We
developed a dataset of 30 horses that allowed for both "within-domain" and
"out-of-domain" (unseen horse) benchmarking - this is a crucial test for
robustness that current human pose estimation benchmarks do not directly
address. We show that better ImageNet-performing architectures perform better
on both within- and out-of-domain data if they are first pretrained on
ImageNet. We additionally show that better ImageNet models generalize better
across animal species. Furthermore, we introduce Horse-C, a new benchmark for
common corruptions for pose estimation, and confirm that pretraining increases
performance in this domain shift context as well. Overall, our results
demonstrate that transfer learning is beneficial for out-of-domain robustness.
