EAGER: Leveraging Synthetic Data for Visual Reasoning and Representation Learning with Minimal Human Supervision

这个项目研究如何使用计算机图形创建的合成数据可以用于开发理解视觉数据的算法。合成数据提供了现实世界图像难以获得的灵活性，并使探索仅使用现实世界图像难以解决的问题成为可能。该项目开发了用于对象遮挡推理的新算法，以及用于自监督表示学习的新算法，其中在没有人工注释语义标签的帮助下开发有用的图像特征。该项目提供了新的算法，有可能在自主系统和安全应用中受益。除了科学影响外，该项目还开展了补充教育和推广活动，吸引学生参与研究和STEM。本研究探索了从视觉推理和表征学习的合成数据中学习的新算法。虽然在计算机视觉中使用合成数据已经有很长的历史，但它主要用于补充自然图像数据以解决标准任务。相比之下，该项目使用合成数据在相对未探索的问题上取得进展，在这些问题上，给定真实世界的图像很难获得地面真相。该项目包括三个主要重点，每个重点都利用了这样一个事实，即用户可以完全控制合成数据集中发生的一切。在第一篇文章中，作者研究了一种使用合成数据进行表征学习的新方法，在第二篇文章中，作者扩展了该算法，以区分特定任务和通用功能。最后，在推力III中，它探索了一种新的方法来推理物体遮挡。

This project investigates how synthetic data created using computer graphics can be used for developing algorithms that understand visual data. Synthetic data provides flexibility that is difficult to obtain with real-world imagery, and enables opportunities to explore problems that would be difficult to solve with real-world imagery alone. This project develops new algorithms for reasoning about object occlusions, and for self-supervised representation learning, in which useful image features are developed without the aid of human-annotated semantic labels. The project provides new algorithms that have the potential to benefit applications in autonomous systems and security. In addition to scientific impact, the project performs complementary educational and outreach activities that engage students in research and STEM.This research explores novel algorithms that learn from synthetic data for visual reasoning and representation learning. While the use of synthetic data has a long history in computer vision, it has mainly been used to complement natural image data to solve standard tasks. In contrast, this project uses synthetic data to make advances in relatively unexplored problems, in which ground-truth is difficult to obtain given real-world imagery. The project consists of three major thrusts, each of which exploits the fact that a user has full control of everything that happens in a synthetic dataset. In Thrust I, it investigates a novel approach to representation learning using synthetic data, and in Thrust II, it extends the algorithm to disentangle task-specific and general-purpose features. Finally, in Thrust III, it explores a novel approach for reasoning about object occlusions.