Continual Human Pose Estimation for Incremental Integration of Keypoints and Pose Variations
<|reference_start|>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 3.60\% 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.<|reference_end|>
@article{khan2024continual,
    title={Continual Human Pose Estimation for Incremental Integration of Keypoints
  and Pose Variations},
    author={Muhammad Saif Ullah Khan, Muhammad Ahmed Ullah Khan, Muhammad Zeshan
  Afzal, Didier Stricker},
    journal={arXiv preprint arXiv:2409.20469},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20469},
    primaryClass={cs.CV}
}