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Continual Human Pose Estimation for Incremental Integration of Keypoints and Pose Variations

This paper reformulates cross-dataset human pose estimation as a continual learning task, aiming to integrate new keypoints and pose variations into existing models without losing accuracy on previously learned datasets. We benchmark this formulation against established regularization-based methods for mitigating catastrophic forgetting, including EWC, LFL, and LwF. Moreover, we propose a novel regularization method called Importance-Weighted Distillation (IWD), which enhances conventional LwF by introducing a layer-wise distillation penalty and dynamic temperature adjustment based on layer importance for previously learned knowledge. This allows for a controlled adaptation to new tasks that respects the stability-plasticity balance critical in continual learning. Through extensive experiments across three datasets, we demonstrate that our approach outperforms existing regularization-based continual learning strategies. IWD shows an average improvement of 0.71% over the state-of-the-art LwF method. The results highlight the potential of our method to serve as a robust framework for real-world applications where models must evolve with new data without forgetting past knowledge.

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TL;DR

1. We reformulate cross-dataset human pose estimation as a continual learning task.
2. We propose a novel Importance-Weighted Distillation method for incremental integration of keypoints and pose variations.
3. Our approach outperforms existing regularization-based continual learning strategies by 0.71% on average.

How to Cite

If you find this useful, please include the following citation in your work:

@article{khan2024continual,
  title={Continual Human Pose Estimation for Incremental Integration of Keypoints and Pose Variations},
  author={Khan, Muhammad Saif Ullah and Khan, Muhammad Ahmed Ullah and Stricker, Didier and Afzal, Muhammad Zeshan},
  journal={OpenReview Preprint},
  year={2024}
}