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Innovation Together
Since its establishment in 1998, Microsoft Research Asia’s trademark and long term commitment has been to foster innovative research and advanced education in the Asia-Pacific region. Through open collaboration and partnership with universities, government and other academic partners, MSRA has been consistently advancing the state-of-the-art in computer science. This book was compiled to record these outstanding collaborations, as Microsoft Research Asia celebrates its 10th Anniversary. The selected papers are all authored or co-authored by faculty members or students through collaboration with MSRA lab researchers, or with the financial support of MSRA. Papers previously published in top-tier international conference proceedings and journals are compiled here into one accessible volume of outstanding research. Innovation Together highlights the outstanding work of Microsoft Research Asia as it celebrates ten years of achievement and looks forward to the next decade of success.
Text and Ritual in Early China
In Text and Ritual in Early China, leading scholars of ancient Chinese history, literature, religion, and archaeology consider the presence and use of texts in religious and political ritual. Through balanced attention to both the received literary tradition and the wide range of recently excavated artifacts, manuscripts, and inscriptions, their combined efforts reveal the rich and multilayered interplay of textual composition and ritual performance. Drawn across disciplinary boundaries, the resulting picture illuminates two of the defining features of early Chinese culture and advances new insights into their sumptuous complexity. Beginning with a substantial introduction to the conceptual ...
Computer Vision – ECCV 2018
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
Visual Domain Adaptation in the Deep Learning Era
Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularit...
Social Media Retrieval
This comprehensive text/reference examines in depth the synergy between multimedia content analysis, personalization, and next-generation networking. The book demonstrates how this integration can result in robust, personalized services that provide users with an improved multimedia-centric quality of experience. Each chapter offers a practical step-by-step walkthrough for a variety of concepts, components and technologies relating to the development of applications and services. Topics and features: introduces the fundamentals of social media retrieval, presenting the most important areas of research in this domain; examines the important topic of multimedia tagging in social environments, including geo-tagging; discusses issues of personalization and privacy in social media; reviews advances in encoding, compression and network architectures for the exchange of social media information; describes a range of applications related to social media.
Domain Adaptation in Computer Vision Applications
This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an international selection of pre-eminent experts in the field, addressing not only classical image categorization, but also other computer vision tasks such as detection, segmentation and visual attributes. Topics and features: surveys the complete field of visual DA, including shallow methods designed for homogeneous and heterogeneous data as well as deep architectures; presents a positioning of the dataset bias in the CNN-based feature arena; proposes de...
Semantic Mining Technologies for Multimedia Databases
Provides an introduction to recent techniques in multimedia semantic mining necessary to researchers new to the field.
