CAREER: Weakly Supervised Recognition

职业：弱监督识别

• 批准号: 0448609
• 负责人: Nuno Vasconcelos
• 金额: --
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-15 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0448609&HistoricalAwards=false
• 关键词: CAREER; Weakly; Supervised; Recognition

The design of systems that can detect and recognize objects in large image and video repositories will enable significant developments in areas as diverse as life sciences, surveillance and law enforcement, entertainment, advertisement, and copyright protection, among others. While significant progress has been achieved in this area over the last decades, the design of such systems still requires vast amounts of expert knowledge and manual labor. This project lays the foundation for a long-term vision of recognition systems containing banks of recognition modules fully trainable by naive users, with minimal requirements in terms of manual data pre-processing and computational complexity. From a technical standpoint, the project addresses two fundamental barriers in the path to this objective: 1) the dependence of current classifiers on carefully assembled and pre-processed training sets, and 2) the training complexity of state-of-the-art classification architectures. The first is addressed through the introduction of a new statistical learning framework, denoted by weakly supervised assembly of training sets, which combines elements of discriminant visual saliency and image matching to automate the process of assembling the training sets required for detection and recognition. The second is addressed through the introduction of new, and computationally efficient, boosting methods for the design of cascades of large-margin classifiers, with support for both template and constellation-based object representations. At the educational level, the project will contribute to the advancement of the coverage of the recognition problem, through the introduction of new courses, and the development of a software library to be used as a teaching aid in visual information retrieval courses. The design of this library will also provide research opportunities for students from underrepresented backgrounds at a scale well beyond what can usually be found at the undergraduate level.

可以在大图像和视频存储库中检测和识别对象的系统的设计将使生活科学，监视和执法，娱乐，广告以及版权保护等各种各样的领域进行重大发展。尽管在过去几十年中，在这一领域取得了重大进展，但此类系统的设计仍然需要大量的专家知识和体力劳动。该项目为识别系统的长期愿景奠定了基础，该识别系统包含由幼稚用户完全训练的识别模块库，在手动数据预处理和计算复杂性方面的要求最少。从技术的角度来看，该项目在这一目标的道路上解决了两个基本障碍：1）当前分类器对精心组装和预处理的培训集的依赖性，以及2）最先进的分类体系结构的培训复杂性。 首先是通过引入新的统计学习框架来解决的，该框架用弱监督的训练集的组装表示，该培训集结合了判别视觉显着性和图像匹配的要素，以使组装训练集所需的检测和识别所需的训练集。第二个是通过引入新的和计算高效的增强方法来设计的，用于设计大利润分类器的级联反应，并支持基于模板和基于星座的对象表示。在教育层面上，该项目将通过引入新课程以及用于视觉信息检索课程的教学帮助的软件库的开发，从而有助于识别问题的覆盖范围。该图书馆的设计还将为来自代表性不足背景的学生提供研究机会，远远超出了本科生通常可以找到的。