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.

本研究将跨数据集的人类姿态估计重新定义为一个连续学习任务，旨在在不损失以前学过的数据集准确性的情况下，将新的关键点和姿态变化整合到现有模型中。我们提出了一种新的正则化方法—重要性加权蒸馏（IWD），通过引入层级蒸馏惩罚和基于层重要性的动态温度调整，显著提升了现有方法的性能，展现了在不遗忘旧知识的前提下适应新任务的能力。

连续人类姿态估计以增量整合关键点和姿态变化