Orthopedic disorders are a common cause for euthanasia among horses, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle but 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 labeler to provide accurate ground-truth for the data. We show that transferring features from a dataset of horses with acute nociceptive pain (where labeling is less ambiguous) can aid the learning to recognize more complex 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 acute 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:矫形障碍是马中安乐死的一种常见原因,通过早期检测往往可以避免。这些条件往往造成不同程度的微妙但长期疼痛。用描述这种疼痛的视频数据来训练视觉疼痛识别方法十分困难,因为由此产生的疼痛行为也是微妙的,很少出现,而且差异很大,甚至专家人类标签师都难以提供准确的地面真实数据。我们显示,从具有急性无感疼痛(标签不太模糊)的马数据集中转移特征,有助于学习识别更复杂的心肌疼痛。此外,我们提出了这一问题的人类专家基线,以及广泛的经验研究各种领域转移方法,以及根据在矫正数据集中就急性疼痛所训练的疼痛识别方法所检测到的东西。最后,我们讨论的是真实世界动物行为数据集构成的挑战,以及如何为类似的细微行动识别任务确立最佳做法。我们的代码可在 https://github.com/sofiabroome/painfacescience上查阅。