RI:Small: Modeling and Relating Visual Tasks

在大规模视觉数据集上训练的深度网络正在越来越多的应用中使用，如自动驾驶、机器人、制造业、电子商务以及科学和工程学科。然而，探索一个新问题的解决方案的巨大空间可能是困难的，因为它需要大量的计算资源。迫切需要有工具来提高对一项任务的解决方案推广到新任务的程度的理解，沿着有方法来分享设计这些解决方案所需的专业知识。该项目旨在通过开发一个框架来应对这些挑战，该框架可以在广泛的视觉领域中对识别任务进行建模，关联和可视化。这样做将使从业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。该项目将研究该框架的实际应用，并研究检测和适应机器学习模型长期部署中发生的统计变化的方法。具体而言，该项目将着眼于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对研究生和本科生进行教学和指导，并通过大学的早期研究学者计划对未被充分代表的本科生进行指导。具体而言，研究团队将开发：1）将任务嵌入到欧几里得和双曲向量空间的理论框架（创建“任务嵌入”）通过评估用于解决它们的深度网络的参数的重要性; 2）用于计算包含数百万甚至数十亿参数的网络的任务嵌入的有效方法; 3）当标签可用性有限时，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（诸如对象检测和图像分割）的任务嵌入的技术; 5）应用任务嵌入来解决元任务，如数据集选择，多任务处理和检测任务转移;六、通过广泛使用的计算机视觉数据集表示的任务之间的对称和非对称关系的可视化。该奖项反映了NSF的法定使命，并已被视为通过使用基金会的知识价值和更广泛的影响审查标准进行评估，

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.