一种利用额外刺激行为进行基于视频的神经发育多样性分类的新数据集
A Novel Dataset for Video-Based Neurodivergent Classification Leveraging Extra-Stimulatory Behavior
AI总结：
本文提出Video ASD视频数据集，利用卷积和注意力图特征，结合GPU和普通摄像机实现ASD分类，并验证了模型泛化能力及基础模型在运动噪声下的表现。
摘要：
不同个体在对外部刺激作出反应时，其面部表情和动作在强度上存在差异，尤其是在神经发育多样的人群中。此类行为会影响人们的整体健康、沟通和感觉处理。深度学习可以被负责任地用于提高解决这一任务的效率，并帮助医疗专业人员准确理解此类行为。在这项工作中，我们引入了Video ASD数据集——一个包含视频帧卷积和注意力图特征数据的数据集——以促进ASD分类任务的进一步发展。与许多近期使用MRI数据进行ASD分类的研究不同，这些研究需要昂贵的专用设备，我们的方法利用功能强大但相对实惠的GPU、标准计算机设置和摄像机进行推理。结果表明，我们的模型能够有效泛化并理解儿童不同动作中的关键差异。此外，我们在此数据上测试了基础模型，以展示运动噪声如何影响性能，以及对更多数据和更复杂标签的需求。
英文摘要：
Facial expressions and actions differ among different individuals at varying degrees of intensity given responses to external stimuli, particularly among those that are neurodivergent. Such behaviors affect people in terms of overall health, communication, and sensory processing. Deep learning can be responsibly leveraged to improve productivity in addressing this task, and help medical professionals to accurately understand such behaviors. In this work, we introduce the Video ASD dataset-a dataset that contains video frame convolutional and attention map feature data-to foster further progress in the task of ASD classification. Unlike many recent studies in ASD classification with MRI data, which require expensive specialized equipment, our method utilizes a powerful but relatively affordable GPU, a standard computer setup, and a video camera for inference. Results show that our model effectively generalizes and understands key differences in the distinct movements of the children. Additionally, we test foundation models on this data to showcase how movement noise affects performance and the need for more data and more complex labels.
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。
