CIF: Small: Collaborative Research: Sparse and Low Rank Methods for Imbalanced and Heterogeneous Data

CIF：小型：协作研究：针对不平衡和异构数据的稀疏和低秩方法

• 批准号: 1922840
• 负责人: Vishal Patel
• 金额: $ 6万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922840&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Sparse

In recent years, sparse and low-rank modeling techniques have emerged as powerful tools for efficiently processing visual data in non-traditional ways. A particular area of promise for these theories is visual recognition, where object detection and image classification approaches need to be able to deal with the highly diverse appearance of real-world objects. However, existing visual recognition methods generally succeed only in the presence of sufficient amounts of homogeneous and balanced training data that are well matched to the actual test conditions. In practice, when the data are heterogeneous and imbalanced, the performance of existing methods can be much worse than expected.This project will develop a comprehensive framework for real-world visual recognition based on novel sparse and low-rank modeling techniques, which will be able to deal with imbalanced, heterogeneous and multi-modal data. Imbalanced data will be handled using convex optimization techniques that automatically divide a dataset into common and rare patterns, and select a small set of representatives for the common patterns that are then combined with the rare patterns to form a balanced dataset. Heterogeneous and multi-modal data will be handled using non-convex optimization techniques that learn a latent representation from multiple domains or modalities. Classification and clustering algorithms can be applied to the latent representation. Applications of these methods include image and video-based object recognition, activity recognition, video summarization, and surveillance.

近年来，稀疏和低阶建模技术已经成为以非传统方式高效处理视觉数据的有力工具。这些理论的一个特别有希望的领域是视觉识别，其中目标检测和图像分类方法需要能够处理现实世界中高度多样化的外观。然而，现有的视觉识别方法通常只有在存在与实际测试条件很好匹配的足够量的同质和平衡的训练数据时才能成功。在实际应用中，当数据是异质和不平衡的时候，现有方法的性能可能会比预期的要差得多。本项目将基于新的稀疏和低阶建模技术开发一个全面的真实世界视觉识别框架，能够处理不平衡、异质和多模式的数据。不平衡数据将使用凸优化技术进行处理，该技术自动将数据集划分为常见模式和稀有模式，并为常见模式选择一小部分代表，然后将其与稀有模式组合形成平衡数据集。将使用从多个域或多个模式学习潜在表示的非凸优化技术来处理异类和多模式数据。分类和聚类算法可以应用于潜在表示。这些方法的应用包括基于图像和视频的对象识别、活动识别、视频摘要和监控。