EAGER: Quantifying and Reducing Data Bias in Object Detection Using Physics-based Image Synthesis

该项目开发了改进的计算机视觉方法，用于自动识别来自现实环境的图像中的任意对象。对象识别通常通过拟合将图像映射到可能的对象位置和标签的函数来执行。这样的函数在示例图像的数据库上拟合（训练），所述示例图像沿着有它们的人类分配的对象位置和标签。这项研究可以为与社会相关的应用带来更准确的视觉感知，例如机器人执行家务，帮助老年人，应对灾难以及快速学习新的制造和服务技能。它还可以为更广泛的社区提供一个共同的代码库，为领域适应问题提供新的数据集挑战，传播科学和技术成果和相关课件，以及具体的外联活动，以确保代表性不足的群体的广泛参与。第一个目标是建立在任意域上人类水平性能所需的数据集中潜在物理因素的覆盖范围上的界限。这项研究涉及现有的数据集和新的数据集生成的图形渲染技术在不同程度的照片。我们的目标是开发一个给定的数据集的物理复杂性的理论，以及它如何影响泛化到真实的世界的对象识别任务，相对于一个给定的图像表示和学习框架。物理参数包括但不限于：3D形状、表面颜色、纹理、背景/场景、相机视点、传感器噪声、照明、镜面反射和投射阴影。第二个研究目标是学习图像表示不变的一些物理原因的数据偏差。我们的目标是开发能够从真实的和非真实感合成数据的组合中学习的模型和表示学习方法，并且能够抵抗常见的数据偏差来源。这些表示包括简单的基于边缘的描述符，以及更一般地基于卷积和池化操作层的分层表示。

This project develops improved computer vision methods for automatic recognition of arbitrary objects in images from realistic environments. Object recognition is typically performed by fitting a function that maps an image to likely object locations and labels. Such a function is fitted (trained) on a database of example images along with their human-assigned object locations and labels. This research can result in more accurate visual perception for socially relevant applications, such as robots performing household tasks, assisting the elderly, responding to disasters and quickly learning new manufacturing and service skills. It can also provide a common codebase for the wider community, new dataset challenges for domain adaptation problems, the dissemination of scientific and technical results and associated courseware, and specific outreach to ensure broad participation of underrepresented groups.The specific research agenda is structured around two aims. The first aim is to establish bounds on the coverage of latent physical factors in datasets needed for human-level performance on arbitrary domains. The study involves both existing datasets and new datasets generated using graphics rendering techniques at various degrees of photorealism. The goal is to develop a theory of the physical complexity of a given dataset and how it affects generalization to real world object recognition tasks, with respect to a given image representation and learning framework. Physical parameters include but are not limited to: 3D shape, surface color, texture, background/scene, camera viewpoint, sensor noise, lighting, specularities and cast shadows. The second research aim is to learn image representations invariant to some of the physical causes of data bias. The goal is to develop model and representation learning methods that are able to learn from a combination of real and non-photorealistic synthetic data, and are resistant to common sources of data bias. The representations include simple edge-based descriptors, and more generally hierarchical representations based on layers of convolution and pooling operations.