  Animal pose estimation is an important field that has received increasing
attention in the recent years. The main challenge for this task is the lack of
labeled data. Existing works circumvent this problem with pseudo labels
generated from data of other easily accessible domains such as synthetic data.
However, these pseudo labels are noisy even with consistency check or
confidence-based filtering due to the domain shift in the data. To solve this
problem, we design a multi-scale domain adaptation module (MDAM) to reduce the
domain gap between the synthetic and real data. We further introduce an online
coarse-to-fine pseudo label updating strategy. Specifically, we propose a
self-distillation module in an inner coarse-update loop and a mean-teacher in
an outer fine-update loop to generate new pseudo labels that gradually replace
the old ones. Consequently, our model is able to learn from the old pseudo
labels at the early stage, and gradually switch to the new pseudo labels to
prevent overfitting in the later stage. We evaluate our approach on the TigDog
and VisDA 2019 datasets, where we outperform existing approaches by a large
margin. We also demonstrate the generalization ability of our model by testing
extensively on both unseen domains and unseen animal categories. Our code is
available at the project website.
