机器学习从图像中进行行为和心理状态分类的挑战和机遇
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images
摘要：
计算机视觉（CV）分类器可以区分和检测非语言的社会人类行为和精神状态，可以帮助精神病学和行为科学的数字诊断和治疗。虽然可以使用标准机器学习管道开发用于传统和结构化分类任务的CV分类器，用于监督学习，包括数据标记，预处理和训练卷积神经网络，但在尝试此过程进行行为表型分析时会出现几个痛点。在这里，我们讨论了这个领域的挑战和相应的机会，包括处理异构数据，避免有偏见的模型，标记大量和重复的数据集，使用模糊或复合类标签，管理隐私问题，创建适当的表示和个性化模型。我们讨论了当前CV领域最先进的研究成果，如数据策展、数据增强、众包标签、主动学习、强化学习、生成模型、表示学习、联邦学习和元学习。我们强调至少一些机器学习的进步，需要成像分类器成功和可靠地检测人类的社会线索。
英文摘要：
Computer Vision (CV) classifiers which distinguish and detect nonverbal social human behavior and mental state can aid digital diagnostics and therapeutics for psychiatry and the behavioral sciences. While CV classifiers for traditional and structured classification tasks can be developed with standard machine learning pipelines for supervised learning consisting of data labeling, preprocessing, and training a convolutional neural network, there are several pain points which arise when attempting this process for behavioral phenotyping. Here, we discuss the challenges and corresponding opportunities in this space, including handling heterogeneous data, avoiding biased models, labeling massive and repetitive data sets, working with ambiguous or compound class labels, managing privacy concerns, creating appropriate representations, and personalizing models. We discuss current state-of-the-art research endeavors in CV such as data curation, data augmentation, crowdsourced labeling, active learning, reinforcement learning, generative models, representation learning, federated learning, and meta-learning. We highlight at least some of the machine learning advancements needed for imaging classifiers to detect human social cues successfully and reliably.
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。
