RI: Small: Unsupervised Object Class Discovery via Bottom-up Multiple Class Learning

该项目开发了一个集成框架，用于在无监督环境中执行同步对象发现和检测器训练。它利用大量（数百万甚至数十亿）组织良好的互联网图像来自动学习各种对象的丰富图像表示。本项目的主要活动包括以下内容。(1)该项目的核心组成部分是将无监督数据转化为弱监督“噪声输入”的公式，通过该公式，使用新的学习方法探索丰富对象表示的共性。(2)通过互联网搜索引擎使用数千个目标词检索到大量图像，从中学习一个大型的中级图像表示字典。(3)提出了一种新的柔性对象表示方法来处理铰接/非刚性对象。该项目通过开发一种无监督范式来探索大规模的互联网图像，从而推动了计算机视觉和机器学习领域的发展。从数千字检索的图像中学习到的中级和高级表示可以显著提高目标表示能力，使目标识别领域的研究人员受益。该项目得出的公式、算法和方法对医学成像和数据挖掘等其他领域的研究人员也有帮助。项目传播计划包括源代码和学习的中高层表示。

This project develops an integrated framework to perform simultaneous object discovery and detector training in an unsupervised setting. It takes advantages of large amount (millions or even billions) of well-organized internet images to automatically learn rich image representations for a wide range of objects. The main activities in this project include the following. (1) The central component of this project is a formulation to turn unsupervised data into weakly-supervised "noisy input" through which commonalities are explored for rich object representation using a new learning method. (2) A large dictionary of mid-level image representations will be learned on a large scale number of images retrieved using thousands of object words through the internet search engine. (3) A new flexible object representation is developed to deal with articulated/non-rigid objects.The project advances computer vision and machine learning fields by developing an unsupervised paradigm to explore a large scale of internet images. The learned mid-level and high-level representations from images retrieved using thousands of words can significantly enhance the object representation power and benefit researchers in the object recognition field. The formulations, algorithms, and methods resulted from this project are also helpful to researchers in other fields such as medical imaging and data mining. The project dissemination plan includes the source code and learned mid-level and high-level representations.