Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.
翻译:在马中,矫形紊乱很常见,往往会导致安乐死,而早期的检测往往可以避免这种紊乱。这些状况往往造成不同程度的微妙长期疼痛。用描述这种疼痛的视频数据来训练视觉疼痛识别方法是很困难的,因为由此产生的疼痛行为也是微妙的,很少出现,而且各不相同,使得即使是专家人类标签员也难以提供准确的数据真实地面真实性。我们表明,仅仅以具有急性实验性疼痛的马群(标签不太模糊)为特征的模型,可以帮助识别更微妙的矫形疼痛。此外,我们提出了这一问题的人类专家基线,以及对各种领域转移方法的广泛实验性研究,以及对在矫正数据集中经过清洁实验疼痛培训的疼痛识别方法所检测到的东西。最后,我们还要讨论现实世界动物行为数据集构成的挑战,以及如何为类似的微粒行动识别任务确立最佳做法。我们的代码可在 https://github.com/sophiabro/ome-painfacescience查阅。