CAREER: Towards a Self-Taught Vision System

标题：职业：走向自学的视觉系统PI：Erik Led-Miller摘要使用现代学习技术，现在可以通过基于实例的学习来教授计算机视觉概念。但这一过程既耗时又艰巨。通常，必须手动收集大数据集。在执行新任务时，机器通常不会利用以前学到的知识。当面临新的情况时，系统会灾难性地崩溃。这项研究的目标是使教授视觉系统新技能变得非常容易，并通过利用以前学到的知识来设计能够更快地学习任务的机器。简而言之，目标是开发基本上自学成才的计算机视觉系统。更具体地说，这项研究将集中在以下问题上：从少数例子中学习；利用以前学到的知识来提高在新任务中的表现；学习一个对象的属性，可以用来对其他对象进行推断；自主地获取和组织信息；以及利用跨学科技术来帮助人们摆脱“训练”计算机的负担。这些能力在人类身上是理所当然的，但在当今的计算机系统中代表着严重的缺陷。这项工作的一个中心原则是，一次训练视觉系统一个问题，获得大量训练集，并为每个要学习的任务开发训练范例是不切实际的。在许多情况下，训练数据受到严重限制(亚伯拉罕·林肯的照片有限)。理想情况下，计算机系统应该是自适应的，而不是必须为每个新任务做准备，特别是当这些新任务与以前的任务相似的时候。一些具体的研究领域包括，通过观察其他汽车或人脸的移动来学习从一个例子中识别出任何特定的汽车或人脸；为机器人开发软件，以持续探索视觉世界以及视觉与其他感官之间的相互作用；以及学习以一种以前从未见过的字体识别打字文本，而不需要任何该字体的训练示例。这些努力的共同主线是它们减轻了计算机教师的负担。最终目标是开发能够简单、快速地教授和自主探索的计算机。教育计划将在两个领域开展。第一个领域是少数民族和低收入者的外展，涉及马萨诸塞州一所城市学校的一群学生。第二个领域涉及阿默斯特大学马萨诸塞州大学和研究生层面的课程开发和课程指导。项目网页：http://www.cs.umass.edu/~elm/CAREER

Title: CAREER: Towards a Self-Taught Vision SystemPI: Erik Learned-MillerAbstractUsing modern learning techniques, it is now possible to teach computers visual concepts through example-based learning. But this process is time consuming and arduous. Often large data sets must be manually collected. Machines typically do not take advantage of previously learned knowledge when performing new tasks. And when confronted with a new situation, systems fail catastrophically. The goal of this research is to make it dramatically easier to teach vision systems new skills, and to design machines that can learn tasks faster by leveraging previously learned knowledge. In short, the aim is to develop computer vision systems that are largely self-taught. More specifically, this research will focus on problems such as learning from a small number of examples; using previously learned knowledge to improve performance on novel tasks; learning properties of one object that can be used to make inferences about other objects; acquiring and organizing information autonomously; and leveraging interdisciplinary techniques to help relieve people from the burden of ``training'' computers.These capabilities are taken for granted in human beings, but represent serious shortcomings in today's computer systems. A central tenet of this work is that it is impractical to train vision systems one problem at a time, acquiring large training sets and developing training paradigms for each task to be learned. There are many scenarios in which training data are severely limited (there are limited photos of Abraham Lincoln). And ideally, computer systems should be adaptive, and not have to be prepared for each new task, especially when these new tasks are similar to previous ones. Some specific areas of investigation include learning to recognize any particular car or face from a single example, simply by watching other cars or faces as they move about; developing software for robots to continously explore the visual world and the interactions between vision and the other senses; and learning to recognize typewritten text in a font never seen before, without ANY training examples of that font. The common thread in these efforts is that they relieve the burden on the teacher of the computer. The final goal is to develop computers that can be taught simply and rapidly, and that can explore on their own.Educational initiatives will be developed in two areas. The first area is minority and low-income outreach, involving a group of students at an urban Massachusetts school. The second area involves curriculum development and curriculum guidance at the college and graduate levels at UMass, Amherst.Project web page: http://www.cs.umass.edu/~elm/CAREER