Lei Zhang is a senior researcher in Microsoft Research, working with the Cloud Computing and Storage group on visual recognition and exploring the solutions that can leverage the power of cloud computing.
Prior to this, he has been working with Microsoft Research Asia for 12 years as a senior researcher, leading a research team working on visual recognition, image analysis, and large-scale data mining. His years of work on large-scale search-based image annotation has generated many practical impacts in multimedia search, including a highly scalable solution of duplicate image clustering for billions of images. From 2013 to 2015, he moved to Bing Multimedia Search as a principal development manager, helping develop cutting-edge solutions for web-scale image analysis and recognition problems, including image caption generation and high precision image entity linking.
Lei is a senior IEEE member and a senior ACM member, and has served as editorial board members for Multimedia System Journal, as program co-chairs, area chairs, or committee members for many top conferences. He is the author or co-author of 100+ published papers in fields such as multimedia, computer vision, web search and information retrieval, and holds 40+ U.S. patents for his innovation in these fields.
Lei earned all his degrees (B.E., M.E., and Ph.D) in Computer Science from Tsinghua University, and currently also holds an adjunct professor position in Tianjin University.
Lei Zhang, Yong Rui, Image Search from Thousands to Billions in 20 Years, ACM TOMCCAP 2013, Special Issue on the 20th anniversary of the ACM MM conference.
|Pengfei Xu, Lei Zhang, Kuiyuan Yang, Hongxun Yao, Nested-SIFT for Efficient Image Matching and Retrieval, IEEE Multimedia 2013, Special Issue on Web-Scale Near-Duplicate Search: Techniques and Applications.|
- Arista (lARge-scale Image Search To Annotation)
Arista is a data-driven image annotation system, which annotates an image based on large-scale image search. Its assumption is that close similar images share similar semantics. It targets at a practical image annotation engine which is able to automatically annotate images of any popular concepts. Starting from 2006, Arista is now able to perform online tagging based on 2 billion web images leveraging near-duplicate detection technique.
- MindFinder: Finding Images by Sketching
Sketch-based image search is a well-known and difficult problem, in which little progress has been made in the past decade in developing a large-scale and practical sketch-based search engine. We have revisited this problem and developed a scalable solution to sketch-based image search. The MindFinder system has been built by indexing more than two million web images to enable efficient sketch-based image retrieval, and many creative applications can be expected to advance the state of the art.
- Picto: A large scale visual indexing and recognition system
In this project, we focus on developing algorithms for large-scale image indexing and recognition. Our research covers low-level image features, middle level image representations, and indexing and ranking algorithms.
- Travel Guide
The Travel Guide project has the following mission: Showcase innovations to help users better plan, enjoy, and share their travel experiences. During this process, we transfer technologies to product teams, and drive further research from lessons learned in real world deployment. currently the service is limited to China area, http://travel.msra.cn (in Chinese).
- Website Structure Understanding and its Applications
Website structure understanding can be treated as a reverse engineering for the purpose of automatically discovering the layout templates and URL patterns of a website, and understanding how these templates and patterns are integrated to organize the website. The study of this problem has had a great impact to many applications which can leverage such site-level knowledge to help web search and data mining.