基于弱标注视频的疼痛强度估计序数回归的深度域自适应
Deep Domain Adaptation for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labelled Videos
AI总结：
本文提出WSDA-OR模型，通过序数回归、多实例学习和深度对抗域自适应，利用弱标注目标域视频实现疼痛强度估计，并在多个数据集上验证了其有效性。
摘要：
从视频中捕捉的面部表情估计疼痛强度在医疗保健应用中具有巨大潜力。鉴于面部表情的主观变化以及实际采集条件带来的挑战，最先进的深度学习模型识别面部表情的准确性可能会下降。域自适应已被广泛探索，以缓解通常发生在不同源域和目标域之间采集的视频数据中的域偏移问题。此外，由于收集和标注视频的繁重任务，以及相邻强度级别之间模糊性导致的主观偏差，弱监督学习在此类应用中正受到越来越多的关注。最先进的弱监督学习模型通常被表述为回归问题，既没有利用疼痛强度级别之间的序数关系，也没有利用多个连续帧的时间一致性。本文提出了一种新的深度学习模型，用于具有序数回归的弱监督域自适应，该模型可以使用目标域视频以及周期性提供的粗粒度标签进行自适应。WSDA-OR模型强制目标序列所分配的强度级别之间的序数关系，并将多个相关帧与序列级标签关联起来。特别是，它通过将多实例学习与深度对抗域自适应相结合来学习具有判别性和域不变性的特征表示，其中使用软高斯标签来高效表示来自目标域的弱序数序列级标签。所提出的方法使用RECOLA视频数据集作为完全标注的源域数据，并使用UNBC-McMaster肩痛视频数据集作为弱标注的目标域数据进行了验证。我们还在BIOVID和Fatigue数据集上验证了WSDA-OR用于序列级估计。
英文摘要：
Estimation of pain intensity from facial expressions captured in videos has an immense potential for health care applications. Given the challenges related to subjective variations of facial expressions, and operational capture conditions, the accuracy of state-of-the-art DL models for recognizing facial expressions may decline. Domain adaptation has been widely explored to alleviate the problem of domain shifts that typically occur between video data captured across various source and target domains. Moreover, given the laborious task of collecting and annotating videos, and subjective bias due to ambiguity among adjacent intensity levels, weakly-supervised learning is gaining attention in such applications. State-of-the-art WSL models are typically formulated as regression problems, and do not leverage the ordinal relationship among pain intensity levels, nor temporal coherence of multiple consecutive frames. This paper introduces a new DL model for weakly-supervised DA with ordinal regression that can be adapted using target domain videos with coarse labels provided on a periodic basis. The WSDA-OR model enforces ordinal relationships among intensity levels assigned to target sequences, and associates multiple relevant frames to sequence-level labels. In particular, it learns discriminant and domain-invariant feature representations by integrating multiple instance learning with deep adversarial DA, where soft Gaussian labels are used to efficiently represent the weak ordinal sequence-level labels from target domain. The proposed approach was validated using RECOLA video dataset as fully-labeled source domain data, and UNBC-McMaster shoulder pain video dataset as weakly-labeled target domain data. We have also validated WSDA-OR on BIOVID and Fatigue datasets for sequence level estimation.
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。
