论文标题
人类对象互动检测：快速调查和检查方法
Human-Object Interaction Detection:A Quick Survey and Examination of Methods
论文作者
论文摘要
人类对象的互动检测是计算机视觉和视觉语义信息提取的世界中相对较新的任务。以机器识别人类在对象上执行的相互作用的目标，该领域的研究有许多现实世界中的用例。据我们所知，这是对该领域最新和里程碑的首次一般调查。我们对人类对象相互作用检测领域的发展提供了基本调查。该领域的许多作品都使用多流卷积神经网络体系结构，该架构结合了输入图像中多个来源的特征。最常见的是这些人和对象以及两者的空间质量。据我们所知，尚未进行深入的研究，以研究每个组件的性能。为了向未来的研究人员提供洞察力，我们进行了一项个性化的研究，该研究检查了人类对象相互作用检测的多流卷积神经网络架构的每个组成部分的性能。具体而言，我们检查了HORCNN体系结构，因为它是该领域的基础作品。此外，我们深入探讨了HICO-DET数据集，这是人类对象相互作用检测领域的流行基准。代码和论文可以在https://github.com/shi-labs/human-object-interaction-detection上找到。
Human-object interaction detection is a relatively new task in the world of computer vision and visual semantic information extraction. With the goal of machines identifying interactions that humans perform on objects, there are many real-world use cases for the research in this field. To our knowledge, this is the first general survey of the state-of-the-art and milestone works in this field. We provide a basic survey of the developments in the field of human-object interaction detection. Many works in this field use multi-stream convolutional neural network architectures, which combine features from multiple sources in the input image. Most commonly these are the humans and objects in question, as well as the spatial quality of the two. As far as we are aware, there have not been in-depth studies performed that look into the performance of each component individually. In order to provide insight to future researchers, we perform an individualized study that examines the performance of each component of a multi-stream convolutional neural network architecture for human-object interaction detection. Specifically, we examine the HORCNN architecture as it is a foundational work in the field. In addition, we provide an in-depth look at the HICO-DET dataset, a popular benchmark in the field of human-object interaction detection. Code and papers can be found at https://github.com/SHI-Labs/Human-Object-Interaction-Detection.
