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.

在海量视觉数据集上训练的深度网络正在越来越多地应用于自动驾驶、机器人、制造、电子商务以及科学和工程学科等领域。探索新问题解决方案的广阔空间可以是困难，因为它需要大量的计算资源，迫切需要能够提高对一项任务的解决方案泛化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。通过开发一个框架来建模、关联和解决这些挑战跨广泛的视觉领域可视化识别任务将使实践者能够识别跨任务应用的密切相关的数据集，并选择深度网络架构进行预训练。该项目将检查该框架的实际应用并检查检测方法。具体来说，该项目将研究生态学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教学、指导研究生和本科生。学生通过与项目相关的研究活动，以及该项目旨在创建一个表示各种视觉识别任务及其关系的通用框架，具体而言，研究团队将开发：1）通过以下方式将任务嵌入到欧几里得和双曲向量空间中的理论框架（创建“任务嵌入”）。评估用于解决这些问题的深度网络参数的重要性；2）计算包含数百万个网络的任务嵌入的有效方法，甚至数十亿个参数；3）在标签可用性有限时利用未标记数据增强任务嵌入的技术；4）计算密集视觉预测任务（例如对象检测和图像分割）的任务嵌入的技术；用于解决元任务（例如数据集选择、多任务处理和检测任务转移）的嵌入；6）广泛使用的计算机所代表的任务之间的对称和不对称关系的可视化；该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。