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.

能够检测和识别大型图像和视频存储库中的对象的系统的设计将使生命科学、监视和执法、娱乐、广告和版权保护等领域的重大发展成为可能。虽然在过去的几十年里，这一领域已经取得了重大进展，但设计这样的系统仍然需要大量的专业知识和手工劳动。该项目为识别系统的长期愿景奠定了基础，该系统包含完全可由天真用户训练的识别模块，对手动数据预处理和计算复杂性的要求最低。从技术角度来看，该项目解决了实现这一目标的两个基本障碍：1）当前分类器对精心组装和预处理的训练集的依赖，以及2）最先进的分类架构的训练复杂性。 第一个是解决通过引入一个新的统计学习框架，表示弱监督组装的训练集，它结合了判别视觉显着性和图像匹配的元素，以自动化的过程中组装所需的检测和识别的训练集。第二个问题是通过引入新的，计算效率高，提高方法的级联设计的大利润分类，支持模板和星座为基础的对象表示。在教育一级，该项目将通过开设新课程和开发一个软件库，作为视觉信息检索课程的教学辅助工具，促进扩大识别问题的覆盖面。这个图书馆的设计还将为来自代表性不足的背景的学生提供研究机会，其规模远远超过通常在本科阶段可以找到的。