用立体热成像自动标注，小量人工数据即可让模型快速适配骑车场景
ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling
- 基于立体热成像与多模态数据自动标注，实现运动场景迁移
- 仅用少量人工标注数据微调，模型性能显著提升
- 适合需要快速适配新运动场景的体育医疗研究者
红外热成像在运动医学中日益重要，可评估运动时的热辐射并分析特定解剖区域（如明显暴露的小腿）。在前期自动标注方法基础上，本文将立体与多模态标注方法从跑步机跑步迁移至固定自行车骑行。通过对比不同数据组合下语义分割网络的训练与微调效果，结果表明：仅用少量高质量人工标注数据进行微调，即可显著提升深度神经网络的整体性能。最终，结合自动生成标签与少量人工标注数据，能加速深度神经网络对新应用场景（如从跑步机转向自行车）的适应能力。
原文摘要 · Abstract (English)
Infrared thermography is emerging as a powerful tool in sports medicine, allowing assessment of thermal radiation during exercise and analysis of anatomical regions of interest, such as the well-exposed calves. Building on our previous advanced automatic annotation method, we aimed to transfer the stereo- and multimodal-based labeling approach from treadmill running to ergometer cycling. Therefore, the training of the semantic segmentation network with automatic labels and fine-tuning on high-quality manually annotated images has been examined and compared in different data set combinations. The results indicate that fine-tuning with a small fraction of manual data is sufficient to improve the overall performance of the deep neural network. Finally, combining automatically generated labels with small manually annotated data sets accelerates the adaptation of deep neural networks to new use cases, such as the transition from treadmill to bicycle.
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