用猴子数据做迁移学习，能提升临床人群的人体姿态估计效果
Monkey Transfer Learning Can Improve Human Pose Estimation
- 用猴类姿态数据迁移训练，让模型学更多运动模式
- 仅需1000个真人数据就超越用19185个数据训练的基准模型
- 适合缺乏临床数据时的医学姿态分析场景
本研究探讨了从猕猴迁移学习是否能改善人体姿态估计。当前先进姿态估计方法虽在非临床数据集上接近人工标注精度，但在新情境下表现不佳，难以推广至具有异常运动模式的临床人群。由于伦理问题和数据收集困难，临床数据难以获取。我们发现，使用其他物种的数据可让网络接触更丰富的运动线索，从而提升泛化能力。实验表明，采用跨物种迁移学习后，模型在精确率和召回率上均优于仅用人类数据训练的基准模型；且所需人类训练样本大幅减少（1,000对19,185）。结果表明，猕猴姿态数据有助于提升临床场景中的人体姿态估计性能。未来应进一步探索以猴类数据训练的姿态模型在临床人群中的应用潜力。
原文摘要 · Abstract (English)
In this study, we investigated whether transfer learning from macaque monkeys could improve human pose estimation. Current state-of-the-art pose estimation techniques, often employing deep neural networks, can match human annotation in non-clinical datasets. However, they underperform in novel situations, limiting their generalisability to clinical populations with pathological movement patterns. Clinical datasets are not widely available for AI training due to ethical challenges and a lack of data collection. We observe that data from other species may be able to bridge this gap by exposing the network to a broader range of motion cues. We found that utilising data from other species and undertaking transfer learning improved human pose estimation in terms of precision and recall compared to the benchmark, which was trained on humans only. Compared to the benchmark, fewer human training examples were needed for the transfer learning approach (1,000 vs 19,185). These results suggest that macaque pose estimation can improve human pose estimation in clinical situations. Future work should further explore the utility of pose estimation trained with monkey data in clinical populations.
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