通过感知锚定的风格迁移学习增强古希腊瓶画中的人体姿态估计
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning
AI总结：
本文提出通过感知锚定的风格迁移学习将自然图像适配为希腊瓶画风格，并结合自建ClassArch数据集微调模型，显著提升跨领域人体姿态估计性能。
摘要：
人体姿态估计（HPE）是理解艺术藏品（如希腊瓶画）中人物视觉叙事和身体动作的核心部分。然而，现有HPE方法在跨领域场景下泛化能力不足，导致姿态识别效果较差。为此，我们提出一种两步方法：（1）通过图像风格迁移，将具有已知人物和姿态标注的自然图像数据集适配为希腊瓶画风格。我们引入了一种感知锚定的风格迁移训练，以强化感知一致性。随后，使用这一新构建的数据集对基础模型进行微调。我们表明，采用风格迁移学习可将未标注数据上的SOTA性能显著提升超过6%的平均精度均值（mAP）以及平均召回率均值（mAR）。（2）为进一步提升已经很强的结果，我们构建了一个小型数据集（ClassArch），其中包含公元前6至5世纪古希腊瓶画及其人物和姿态标注。我们表明，在该数据上使用风格迁移后的模型进行微调可进一步提升性能。在一项全面的消融研究中，我们对风格强度的影响进行了针对性分析，揭示出模型学习到了通用的领域风格。此外，我们提供了基于姿态的图像检索，以证明我们方法的有效性。
英文摘要：
Human pose estimation (HPE) is a central part of understanding the visual narration and body movements of characters depicted in artwork collections, such as Greek vase paintings. Unfortunately, existing HPE methods do not generalise well across domains resulting in poorly recognized poses. Therefore, we propose a two step approach: (1) adapting a dataset of natural images of known person and pose annotations to the style of Greek vase paintings by means of image style-transfer. We introduce a perceptually-grounded style transfer training to enforce perceptual consistency. Then, we fine-tune the base model with this newly created dataset. We show that using style-transfer learning significantly improves the SOTA performance on unlabelled data by more than 6% mean average precision (mAP) as well as mean average recall (mAR). (2) To improve the already strong results further, we created a small dataset (ClassArch) consisting of ancient Greek vase paintings from the 6-5th century BCE with person and pose annotations. We show that fine-tuning on this data with a style-transferred model improves the performance further. In a thorough ablation study, we give a targeted analysis of the influence of style intensities, revealing that the model learns generic domain styles. Additionally, we provide a pose-based image retrieval to demonstrate the effectiveness of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
