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

该项目开发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物体。对象识别通常是通过拟合一个函数来执行的，该函数将图像映射到可能的对象位置和标签。这样的函数被拟合（训练）在一个示例图像的数据库上，以及它们的人为分配的对象位置和标签。这项研究可以为社会相关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，传播科技成果和相关课件，以及确保代表性不足的群体广泛参与的具体外展。具体的研究议程围绕两个目标展开。第一个目标是建立在任意领域的人类水平性能所需的数据集中潜在物理因素的覆盖范围的界限。该研究涉及现有数据集和使用不同程度的真实感图形渲染技术生成的新数据集。目标是开发给定数据集的物理复杂性的理论，以及它如何影响对现实世界对象识别任务的泛化，相对于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、镜面和投影。第二个研究目标是学习图像表示不受数据偏差的一些物理原因的影响。目标是开发模型和表示学习方法，能够从真实和非逼真的合成数据中学习，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，以及基于卷积层和池化操作的更一般的分层表示。

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.