Human Pose (HP) estimation is actively researched because of its wide range of applications. However, even estimators pre-trained on large datasets may not perform satisfactorily due to a domain gap between the training and test data. To address this issue, we present our approach combining Active Learning (AL) and Transfer Learning (TL) to adapt HP estimators to individual video domains efficiently. For efficient learning, our approach quantifies (i) the estimation uncertainty based on the temporal changes in the estimated heatmaps and (ii) the unnaturalness in the estimated full-body HPs. These quantified criteria are then effectively combined with the state-of-the-art representativeness criterion to select uncertain and diverse samples for efficient HP estimator learning. Furthermore, we reconsider the existing Active Transfer Learning (ATL) method to introduce novel ideas related to the retraining methods and Stopping Criteria (SC). Experimental results demonstrate that our method enhances learning efficiency and outperforms comparative methods. Our code is publicly available at: https://github.com/ImIntheMiddle/VATL4Pose-WACV2024

翻译：人体姿态（HP）估计因其广泛的应用而备受关注。然而，即使在大型数据集上预训练的估计器，由于训练数据与测试数据之间存在领域差距，其性能也可能不尽如人意。为解决此问题，我们提出了一种结合主动学习（AL）和迁移学习（TL）的方法，以高效地将HP估计器适应于特定视频领域。为了实现高效学习，我们的方法量化了（i）基于估计热图时间变化的估计不确定性，以及（ii）估计全身HP中的非自然性。随后，这些量化标准与最先进的代表性标准有效结合，以选择不确定且多样化的样本，从而实现高效的HP估计器学习。此外，我们重新审视了现有的主动迁移学习（ATL）方法，引入了关于重训练方法和停止准则（SC）的新颖思路。实验结果表明，我们的方法提高了学习效率，并优于对比方法。我们的代码公开于：https://github.com/ImIntheMiddle/VATL4Pose-WACV2024