Biography:

Wen-Huang Cheng received the B.S. and M.S. degrees in computer science and information engineering from National Taiwan University, Taipei, Taiwan, in 2002 and 2004, respectively, where he received the Ph.D. (Hons.) degree from the Graduate Institute of Networking and Multimedia in 2008.

He is currently an Associate Research Fellow with the Research Center for Information Technology Innovation (CITI), Academia Sinica, Taipei, Taiwan, where he is the Founding Leader with the Multimedia Computing Laboratory (MCLab), CITI, and an Assistant Research Fellow with a joint appointment in the Institute of Information Science. Before joining Academia Sinica, he was a Principal Researcher with MagicLabs, HTC Corporation, Taoyuan, Taiwan, from 2009 to 2010. His current research interests include multimedia content analysis, multimedia big data, deep learning, computer vision, mobile multimedia computing, social media, and human computer interaction.

Dr. Cheng has received numerous research awards, including the Outstanding Youth Electrical Engineer Award from the Chinese Institute of Electrical Engineering in 2015, the Top 10% Paper Award from the 2015 IEEE International Workshop on Multimedia Signal Processing, the Outstanding Reviewer Award from the 2015 ACM International Conference on Internet Multimedia Computing and Service, the Prize Award of Multimedia Grand Challenge from the 2014 ACM Multimedia Conference, the K. T. Li Young Researcher Award from the ACM Taipei/Taiwan Chapter in 2014, the Outstanding Young Scholar Awards from the Ministry of Science and Technology in 2014 and 2012, the Outstanding Social Youth of Taipei Municipal in 2014, the Best Reviewer Award from the 2013 Pacific-Rim Conference on Multimedia, the Best Poster Paper Award from the 2012 International Conference on 3D Systems and Applications.



Lectures:

Lecture 1: Sensing Visual Semantics for Interactive Multimedia Applications
For the effective development of interactive multimedia applications, one key technology is the multimedia content analysis, especially its achievable semantic level, i.e. the level of comprehension for a multimedia system on the multimedia content. However, since visual entities like objects in real-world photos and videos are usually captured in uncontrolled conditions, such as various viewpoints, positions, scales, and background clutter, in this lecture, we will present sensing techniques for giving robust visual semantics retrieval and recognition in real-world scenes. Several application scenarios will be showcased to demonstrate the effectiveness of the proposed sensing techniques. In particular, one application is to analyze the fashion trend of clothes from real video contents. Another application is mobile vision for locating the visual objects precisely and meanwhile achieving real-time performances. The third and the last application is video-based human posture and gesture detection to benefit the creation of serious gaming environments for professional training purpose.

Lecture 2: Exploring Social Semantics from Multimedia Big Data
Users are key elements in social multimedia and a huge amount of user-generated multimedia contents (multimedia big data) are created and exchanged through the social interactions among users. Exploring social semantics from multimedia big data is thus an effective way to understanding users and their behaviors. Particularly, popularity prediction on social media is a specific type of social semantics and has attracted extensive attention because of its widespread applications, such as online marketing, trend detection and resource allocation. Generally, given historical user-item pairs, popularity prediction is defined as the problem of estimating the rating scores, view counts or click-through of a newly post in social media. In this lecture, we first review existing research on popularity prediction that predominantly focuses on exploring the correlation between popularity and user-item factors, such as item content, user cues, social relation, and user-item interaction. In fact, time also exerts crucial impact on the popularity but is often overlooked. We further present techniques on investigating the popularity prediction from two complementary perspectives by factoring popularity into two contextual associations, i.e., user-item context and time-sensitive context. The user-item context is linked to popularity with user-specific and item-specific contextual information, which can be derived from user-item sharing behaviors on social media. The time-sensitive context is affected by 'change over time' information (associated with sharing time of photos) including user activeness variability and photo prevalence variability. Finally, further research on exploring social semantics from multimedia big data will be addressed.

Lecture 1: A Light-weight 3D Reconstruction System
3D models allow us to explore all dimensions of the objects, e.g. monuments, sites, even the whole city regions. Over the last decade, a significant number of 3D related approaches and applications, such as 3D printing, 3D films, 3D archive and so on, have become popular research topics. Furthermore, to meet the 3D industry's requirements on high accuracy and flexibility, 3D reconstruction approaches that can be performed anywhere and anytime are in high demand. To achieve this goal, two main challenges need to be overcome, the acquisition of acceptable input data and the computational complexity of the reconstruction procedure. Many researches are devoted to developing the technique of laser scanner calibration. However, the cost of a 3D laser scanner restricts its usage to general since it is not affordable to most people. In addition, due to the size of it, this device cannot be considered as a part of a mobile-based application. In this lecture, we will present a light-weight 3D object reconstruction approach. It aims to fulfill the increasing demand for fast and reliable 3D reconstruction in a mobile environment. Thereby, people can directly use their own portable device to reconstruct the desired objects into 3D models.

Lecture 2: New Intra Coding Schemes for High Efficiency Video Coding
Video coding is a procedure that compresses digital video data to reduce the required bandwidth when transmitting a video. The goal of video coding is to compress a large amount of data efficiently for the transmission of data over the Internet while keeping acceptable visual quality of the reconstructed video. Intra coding has an important role in video coding because it prevents error propagation and maintains better visual quality; in addition, it requires much less computations than inter coding because no motion estimation computations are required. However, the current intra coding methods for HEVC standard is still inefficient, so a new intra coding scheme is required for further improvement of coding efficiency. For these reasons, two new directions including pattern matching and predictive texture synthesis are used to enhance the intra coding efficiency, and to achieve a better coding performance than HEVC intra prediction.