Keynote Speaker - Towards Mobile Augmented Reality

Stanford University

Mobile devices are expected to become ubiquitous platforms for visual search and mobile augmented reality applications. For object recognition on mobile devices, a visual database is typically stored in the cloud. Hence, for a visual comparison, information must be either uploaded from, or downloaded to, the mobile over a wireless link. The response time of the system critically depends on how much information must be transferred in both directions, and efficient compression is the key to a good user experience. We review recent advances in mobile visual search, using compact feature descriptors, and show that dramatic speed-ups and power savings are possible by considering recognition, compression, and retrieval jointly. For augmented reality applications, where image matching is performed continually at video frame rates, interframe coding of SIFT descriptors achieves bit-rate reductions of 1-2 orders of magnitude relative to advanced video coding techniques. We will use real-time implementations for different example applications, such as recognition of landmarks, media covers or printed documents, to show the benefits of implementing computer vision algorithms on the mobile device, in the cloud, or both.

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Introduction to Large Scale Nearest Neighbor Search Problems and Methods

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Keynote Speaker - Reshaping User Experiences with Analytics

Haohong Wang

General Manager, TCL Research America

In the past few years, devices with screens have been getting much smarter, however, far from sufficient for the large screens. Almost all industry giants tried and failed, some hurt badly, in bringing pleasant user experiences to the home screens, thus this trillion-dollar market has not been really conquered so far. Now we are marching into the era of Ultra High-Definition (UHD), the screen size and resolution will increase again significantly, however, the pace of user interaction development seems lag behind. In this talk, we discuss using data analytics to improve user experiences for home entertainment. With the incorporate of analytics components, such as user behaviors learning and mining, user preference understanding, media low-level features and high-level semantics extraction, object detection and recognition, media recognition, and real-time recommendation and so on, we showcase that user experience innovations can be achieved to make the devices with screens much more user friendly.

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Supporting Media Bricoleurs with Cemint

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