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

EAGER：在最少的人类监督下利用合成数据进行视觉推理和表示学习

• 批准号: 1748387
• 负责人: Yong Jae Lee
• 金额: $ 20万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1748387&HistoricalAwards=false
• 关键词: EAGER; Leveraging; Synthetic; Data; Visual

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.

该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。合成数据提供了使用真实世界图像难以获得的灵活性，并使人们有机会探索仅使用真实世界图像难以解决的问题。该项目开发了用于推理对象遮挡和自我监督表示学习的新算法，其中在没有人类注释的语义标签的帮助下开发了有用的图像特征。该项目提供了新的算法，有可能有利于自治系统和安全性的应用。除了科学影响，该项目还开展补充教育和推广活动，让学生参与研究和STEM。这项研究探索了从合成数据中学习视觉推理和表征学习的新算法。虽然合成数据的使用在计算机视觉中有着悠久的历史，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来推进相对未探索的问题，其中地面实况很难获得真实世界的图像。该项目包括三个主要目标，每个目标都利用了用户可以完全控制合成数据集中发生的一切的事实。在Thrust I中，它研究了一种使用合成数据进行表示学习的新方法，在Thrust II中，它扩展了该算法，以区分特定任务和通用功能。最后，在Thrust III中，它探索了一种推理对象遮挡的新方法。

项目成果

HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-Scale Point Clouds

• DOI: 10.1109/cvpr.2019.00337
• 发表时间: 2019-06
• 期刊: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Xiuye Gu;Yijie Wang;Chongruo Wu;Yong Jae Lee;Panqu Wang

SinGAN-GIF: Learning a Generative Video Model from a Single GIF

• DOI: 10.1109/wacv48630.2021.00135
• 发表时间: 2021-01
• 期刊: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
• 作者: Rajat Arora;Yong Jae Lee

You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection

• DOI: 10.1109/cvpr.2019.00964
• 发表时间: 2019-06
• 期刊: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Krishna Kumar Singh;Yong Jae Lee

Boxer: Preventing fraud by scanning credit cards

Boxer：通过扫描信用卡防止欺诈

• 发表时间: 2020
• 期刊: USENIX Security Symposium
• 作者: Abi Din, Zainul;Venugopalan, Hari;Park, Jaime;Li, Andy;Yin, Weisu;Mai, Haohui;Lee, Yong Jae;Liu, Steven;King, Samuel
• 通讯作者: King, Samuel

FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery

• DOI: 10.1109/cvpr.2019.00665
• 发表时间: 2018-11
• 期刊: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Krishna Kumar Singh;Utkarsh Ojha;Yong Jae Lee