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

• URL: http://arxiv.org/abs/2409.20469v1
• Date: Mon, 30 Sep 2024 16:29:30 GMT
• Title: Continual Human Pose Estimation for Incremental Integration of Keypoints and Pose Variations
• Authors: Muhammad Saif Ullah Khan, Muhammad Ahmed Ullah Khan, Muhammad Zeshan Afzal, Didier Stricker,
• Abstract summary: This paper reformulates cross-dataset human pose estimation as a continual learning task. We benchmark this formulation against established regularization-based methods for mitigating catastrophic forgetting. We show that our approach outperforms existing regularization-based continual learning strategies.
• Score: 12.042768320132694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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