From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation (2103.14843v1)

Abstract: 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.

• The paper proposes an unsupervised domain adaptation method to bridge the gap between synthetic and real data for challenging animal pose estimation without extensive labels.
• The approach integrates a multi-scale domain adaptation module and an innovative coarse-to-fine pseudo label updating strategy using student-teacher learning and MixUp.
• Evaluations demonstrate significant performance gains over prior unsupervised methods, improving accuracy for horses by 12.34% and showing strong generalization to unseen animal categories.

Unsupervised Domain Adaptation for Animal Pose Estimation

The paper "From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation," authored by Chen Li and Gim Hee Lee from the National University of Singapore, addresses a challenging problem in animal pose estimation: the scarcity of labeled data. Traditional methods in pose estimation, particularly for humans, leverage labeled datasets to train deep learning models. However, such labeled data is scant for non-human subjects like animals due to the complexities involved in manual annotation and the diversified appearance of animals.

The approach proposed in this paper hinges on unsupervised domain adaptation (UDA) to translate learned pose estimation models from synthetic datasets—where poses can be conveniently generated and labeled—to real-world, unlabeled datasets. The authors introduce a multi-scale domain adaptation module (MDAM) and an innovative coarse-to-fine pseudo label updating strategy to tackle the inherent domain gap between synthetic and real images.

Technical Approach

1. Multi-Scale Domain Adaptation Module: The MDAM integrates a pose estimation module and a domain classifier. It utilizes feature maps at multiple scales to ensure both global and local features can be aligned across domains. The domain classifier aims to generate domain-invariant features by minimizing the discrepancy between synthetic and real-world domain classifications.
2. Coarse-to-Fine Pseudo Label Updating Strategy:
   • Inner Coarse-Update Loop: This loop involves a self-distillation module comprising a refinement block and a self-feedback loop. It starts by training with initial pseudo labels derived from synthetic data and gradually transitions to refined pseudo labels produced by the refinement block.
   • Outer Fine-Update Loop: This loop adopts a student-teacher framework. The teacher network is updated with the exponential moving average of the student parameters, providing more stable and improved pseudo labels for further training iterations.
3. MixUp Regularizer: The model further incorporates MixUp, a data augmentation technique where pseudo labels are mixed with ground truth labels from the synthetic domain to bolster robustness against label noise.

Experimental Evaluation

The proposed method outperforms existing UDA techniques significantly, verified through evaluations on the TigDog and VisDA2019 datasets. For horses, the approach improved over previous methods by 12.34% on average, and exhibited superior generalization to unseen domains and animal categories like those in the Zebra and Animal-Pose datasets.

Implications and Future Directions

The implications of this research extend to various fields such as zoology, biology, and aquaculture where understanding animal movements can lead to insights into behavior and health conditions. The adoption of UDA in pose estimation indicates potential in adapting human-centric models to broader applications involving non-human subjects without the need for extensive annotations.

Future work in AI may look into refining pseudo-label generation, potentially through leveraging self-supervised learning techniques or more sophisticated domain generalization methods. Moreover, exploring other types of synthetic data, possibly encompassing more dynamic environments or varying lighting conditions, could further reduce the domain gap for real-world application.

Conclusion

This paper presents a compelling methodology to bridge the synthetic-real domain gap in animal pose estimation. By innovatively combining domain adaptation techniques with iterative pseudo-label refinement, it sets a benchmark for UDA application in tasks beyond human pose estimation, paving the way for new research trajectories in cross-domain learning.