CAREER: Weakly Supervised Recognition

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取得重大发展。虽然过去几十年在这方面取得了重大进展，但此类系统的设计仍然需要大量的专业知识和体力劳动。该项目为识别系统的长期愿景奠定了基础，该系统包含可由幼稚的用户充分培训的识别模板库，在人工数据预处理和计算复杂性方面的要求最低。从技术角度来看，该项目解决了实现这一目标的道路上的两个基本障碍：1)目前的分类器对精心组装和预处理的训练集的依赖，以及2)最先进的分类体系的训练复杂性。第一个是通过引入一个新的统计学习框架来解决的，该框架表示为训练集的弱监督组装，该框架结合了判别视觉显著和图像匹配的元素，以自动化组装检测和识别所需的训练集的过程。第二个是通过引入新的、在计算上有效的助推方法来解决的，用于设计大间隔分类器的级联，支持模板和基于星座的对象表示。在教育方面，该项目将通过采用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，帮助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背景的学生提供研究机会，其规模远远超出本科水平。

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.