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

该项目开发了一个综合框架，在无人监督的环境中同时执行物体发现和探测器培训。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。该项目的主要活动包括以下内容。(1)本项目的核心部分是将非监督数据转化为弱监督的“噪声输入”，通过使用一种新的学习方法来探索丰富对象表示的共性。(2)将在通过互联网搜索引擎使用数千个目标词检索的大量图像上学习中层图像表示的大词典。(3)开发了一种新的柔性对象表示方法来处理关节/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网络图像，从而推动了计算机视觉和机器学习领域的发展。从使用数千个单词的图像中学习的中层和高级表征可以显著提高物体表征能力，有利于物体识别领域的研究人员。本项目所得到的公式、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级和高级表示法。

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.