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RI: Medium: Creating Knowledge with All-Novel-Class Computer Vision

RI：媒介：利用新颖的计算机视觉创造知识

基本信息

批准号：
2106825
负责人：
David Forsyth
金额：
$ 120万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106825&HistoricalAwards=false
关键词：
RI Medium Creating Knowledge Novel

项目摘要

Computer vision methods have had very high impact on science and industry, but this impact has been confined to cases where there is access to very large quantities of labelled data (i.e., objects are identified in the image), which have either been published, collected or purchased. This research will study computer vision methods that operate in areas where there are very little labelled data. This project builds on the natural model of the way humans and animals learn to label images. One core research goal is an object detection procedure that can be trained with all category data — there will be a small number of examples each from a large number of categories. Another core goal is a learning procedure that can share training examples across categories widely and effectively without explicit linking of the categories. A third core goal is linking learning of early vision tasks — for example, recovering shading and lighting from an image — to learning of classification and detection tasks, so that both tasks can be learned with very little labelled data. Successful completion of this research will unify apparently disparate areas of computer vision, by linking early vision and categorization directly, and will create novel methods for improving categorization performance in difficult circumstances. Furthermore, successful completion of this research will unlock many real-world applications that need all category methods. The all-novel-class problem occurs where there are a small number of examples each from a large number of classes and no class has many examples. This project addresses the all-novel-class issue by sharing of various kinds of information during training. Specifically, three kinds of sharing principles will be studied. The first is a cell consistency principle that uses a geometric and probabilistic analysis of class boundaries driven by feature generation to produce improvements in classification, by requiring that the cells in feature space associated with the similar classes. Second, a label consistency principle uses probabilistic reasoning to impute labels and confidence weights for unlabeled examples. Label consistency can be applied extensively, from category labels for individual examples to superclass labels that identify classes over which sharing will be helpful. Finally, a physical consistency principle requires that inferences from images are consistent with simple physics laws; this principle allows researchers to impute missing annotations for early vision data and links high level classes to early vision through an attribute theory.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.

计算机视觉方法对科学和工业产生了很高的影响，但这种影响仅限于可以访问大量标签数据(即在图像中识别出对象)的情况，这些数据要么已经出版，要么已经收集或购买。这项研究将研究在标签数据非常少的地区运行的计算机视觉方法。这个项目建立在人类和动物学习标记图像的自然模型上。一个核心研究目标是一种可以用所有类别数据训练的对象检测程序-将有少量的样本，每个样本来自大量的类别。另一个核心目标是一种学习程序，它可以广泛和有效地在不同类别之间共享训练实例，而不需要明确地将这些类别联系起来。第三个核心目标是将早期视觉任务的学习--例如，从图像中恢复阴影和光照--与分类和检测任务的学习联系起来，这样，这两项任务都可以用很少的标记数据来学习。这项研究的成功完成将通过将早期视觉和分类直接联系起来，将计算机视觉中明显不同的领域统一起来，并将创造出在困难情况下提高分类性能的新方法。此外，这项研究的成功完成将解锁许多需要所有类别方法的现实世界应用。全新奇类问题出现在有少量的例子，每个例子来自大量的类，并且没有一个类有很多例子的情况下。这个项目通过在培训期间共享各种信息来解决全小说类的问题。具体地说，将研究三种共享原则。第一种是单元一致性原则，它使用由特征生成驱动的类边界的几何和概率分析，通过要求特征空间中的单元与相似类相关联来改进分类。其次，标签一致性原则使用概率推理来为未标记的样本赋予标签和置信度。标签一致性可以广泛应用，从单个示例的类别标签到标识共享将有所帮助的类别的超类标签。最后，物理一致性原则要求来自图像的推论符合简单的物理定律；该原则允许研究人员对早期视觉数据进行缺失的注释，并通过属性理论将高级课程与早期视觉联系起来。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。