2023年6月6日学术报告——王雷

报告题目：Dataset-driven Unsupervised Object Discovery for Region-based Instance Image Retrieval

报告时间：2023年6月6日10:15

报告人：王雷教授

地点：计算机学科楼327会议室

主办单位：bd手机版官网登录ios 、半岛app应用 、半岛电子官网

报告人简介：

王雷，澳大利亚伍伦贡大学计算与信息科技系教授，主要从事计算机视觉与模式识别领域相关研究，现已发表学术文章190余篇，含IEEE TPAMI, IJCV, CVPR, ICCV和ECCV等杂志和会议。王雷博士现为IEEE高级会员、Pattern Recognition Journal编委、多个国际期刊的客座编辑和审稿人。他参与或组织了多个相关领域的国际会议并担任第16届亚洲计算机视觉会议共同程序主席。王雷博士曾获由澳大利亚科学院和澳大利亚国家研究理事会联合颁发的Early Career Researcher Award奖等。

报告内容：

Instance image retrieval could greatly benefit from discovering objects in the image dataset. This not only helps produce more reliable feature representation but also better informs users by delineating query-matched object regions. However, object classes are usually not predefined in a retrieval dataset and class label information is generally unavailable in image retrieval. This situation makes object discovery a challenging task. To address this, we propose a novel dataset-driven unsupervised object discovery framework. By utilizing deep feature representation and weakly-supervised object detection, we explore supervisory information from within an image dataset, construct class-wise object detectors, and assign multiple detectors to each image for detection. To efficiently construct object detectors for large image datasets, we propose a novel “base-detector repository” and derive a fast way to generate the base detectors. In addition, the whole framework is designed to work in a self-boosting manner to iteratively refine object discovery. Compared with existing unsupervised object detection methods, our framework produces more accurate object discovery results. Different from supervised detection, we need neither manual annotation nor auxiliary datasets to train object detectors. Experimental study demonstrates the effectiveness of the proposed framework and the improved performance for region-based instance image retrieval.