RI:Small: Modeling and Relating Visual Tasks

RI:Small：建模和关联视觉任务

• 批准号: 2329927
• 负责人: Subhransu Maji
• 金额: $ 60万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329927&HistoricalAwards=false
• 关键词: RI; Small; Modeling; Relating; Visual

Deep networks trained on massive visual datasets are being used in an increasing number of applications in fields such as autonomous driving, robotics, manufacturing, e-commerce, and science and engineering disciplines. However, exploring the vast space of solutions for a new problem can be difficult as it demands significant computational resources. There is a pressing need for tools that improve understanding of the extent to which solutions from one task generalize to new tasks, along with methods for sharing the expertise needed to design these solutions. This project aims to tackle these challenges by developing a framework to model, relate, and visualize recognition tasks across a broad range of visual domains. Doing so will enable practitioners to identify closely related datasets for application across tasks and to select deep network architectures for pre-training. The project will examine practical applications of the framework and examine methods for detecting and adapting to statistical shifts that take place in long-term deployment of machine-learning models. Specifically, the project will look at efficient solutions for problems in Ecology and Civil Engineering domains. The educational impact of the project includes teaching, mentoring graduate and undergraduate students through research activities associated with the project, and mentoring underrepresented undergraduates in computing through the University's Early Research Scholars Program.This project aims to create a general framework for representing a variety of visual recognition tasks and their relationships. Specifically, the research team will develop: 1) A theoretical framework to embed tasks into Euclidean and hyperbolic vector spaces (creating “task embeddings”) by evaluating the importance of the parameters of deep networks employed to solve them; 2) Efficient methods for computing task embeddings for networks containing millions, or even billions, of parameters; 3) Techniques to leverage unlabeled data to enhance task embeddings when label availability is limited; 4) Techniques to compute task embeddings for dense visual prediction tasks such as object detection and image segmentation; 5) The application of task embeddings to address meta-tasks such as dataset selection, multi-tasking, and detecting task shifts; 6) Visualization of symmetric and asymmetric relationships between tasks represented by widely used computer vision datasets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在自动驾驶，机器人技术，制造业，电子商务以及科学和工程学科等领域的应用中，正在使用大量视觉数据集培训的深网。但是，由于需要大量的计算资源，探索新问题的庞大解决方案可能很困难。迫切需要工具，可以提高对解决方案从一个任务推广到新任务的程度的理解，以及共享设计这些解决方案所需的专业知识的方法。该项目旨在通过开发一个框架来建模，相关和可视化广泛视觉域的识别任务来应对这些挑战。这样做将使实践者能够确定跨任务应用程序的密切相关的数据集，并选择深层网络体系结构进行预训练。该项目将研究框架和考试方法的实际应用，以检测和适应在长期部署机器学习模型中发生的统计转变。具体而言，该项目将研究生态和土木工程领域问题的有效解决方案。该项目的教育影响包括通过与该项目相关的研究活动进行教学，心理研究生和本科生，以及通过大学早期的研究学者计划计算中代表性不足的本科生，该项目旨在创建一个一般框架，以代表各种视觉识别任务及其关系。具体而言，研究团队将开发：1）通过评估用于解决它们的深层网络参数的重要性，将任务嵌入欧几里得和双曲线向量空间（创建“任务嵌入”）的理论框架； 2）用于计算包含数百万甚至数十亿参数网络的任务嵌入的有效方法； 3）当标签可用性有限时，要利用未标记数据来增强任务嵌入的技术； 4）计算密集视觉预测任务的任务嵌入技术，例如对象检测和图像分割； 5）将任务嵌入的应用程序应用于解决元任务，例如数据集选择，多任务和检测任务偏移； 6）可视化对称和不对称关系的任务之间由广泛使用的计算机视觉数据集表示。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估诚实地认为通过评估。