ACM is the professional society of computer scientists, and SIGMM is the special interest group on multimedia.
The 2015 winner of the prestigious ACM Special Interest Group on Multimedia (SIGMM) award for Outstanding Technical Contributions to Multimedia Computing, Communications and Applications is Prof. Dr. Tat-Seng Chua. The award is given in recognition of his pioneering contributions to multimedia, text and social media processing. Tat-Seng Chua is a leading researcher in multimedia, text and social media analysis and retrieval. He is one of the few researchers who has made substantial contributions in the fields of multimedia, information retrieval and social media. Dr. Chua’s contributions in multimedia dates back to the early 1990s, where he was among the first to work on image retrieval with relevance feedback (1991), video retrieval and sequencing by exploring metadata and cinematic rules (1995), and fine grained image retrieval at segment level (1995). These works helped shape the development of the field for many years. Given the limitation of visual content analysis, his research advocates the integration of text, metadata and visual contents coupled with domain knowledge for large-scale media analysis. He developed a multi-source, multi-modal and multi-resolution framework together with the involvement of human in the loop for such analysis and retrieval tasks. This has helped his group not only publish papers in top conferences and journals, but also achieve top positions in large-scale video evaluations when his group participated in TRECVID in 2000-2006, VideOlympics in 2007-09, as well as winning the highly competitive Star (Multimedia) Challenge in 2008. Leveraging the experience, he developed a large-scale multi-label image test set named NUS-WIDE, which has been widely used with over 600 citations. He recently started a company named ViSenze Pte Ltd (www.visenze.com) to commercialize his research in mobile visual fashion search. In his more recent research work in multimedia question-answering (MMQA), he developed a joint text-visual model to exploit correlation between text queries, text-based answers, and visual concepts in images and videos to return both relevant text and video answers. The early work was carried out in the domain of news video (2003), which has motivated several follow-on works in image QA. His recent works tackled the more complicated “how-to” type QA in product domains (2010-13). His recent works (2013-14) exploited SemanticNet to perform attribute-based image retrieval and use of various types of domain knowledge. His current work aims to build a live, continuous-learning system to support the dynamic annotation and retrieval of images and micro videos in social media streams. In information retrieval and social media research, Dr. Chua focused on the key problems of organizing large-scale unstructured text contents to support question-answering (QA). His works point towards the use of linguistics and domain knowledge for effective large-scale information analysis, organization and retrieval. Given his strong interest in both multimedia and text processing, it is natural for him to venture into social media research that involves the analysis of text, multimedia, and social network contents. His group developed a live social observatory system to carry out research in building descriptive, predictive and prescriptive analytics of multiple live social media streams. The system has been well recognized by peers. His recent work on “multi-screen social TV” won the 2015 Best IEEEE Multimedia Best paper Award. Dr. Chua has been involved in most key conferences in these areas by serving as general chair, technical program chair, or invited keynote speaker as well as by leading innovative research and winning many best paper or best student paper awards in recent years. He is the Steering Committee Chair of two international multimedia conference series: ACM ICMR (International Conference on Multimedia Retrieval) and MMM (MultiMedia Modeling). In summary, he is an extraordinarily accomplished and outstanding researcher in multimedia, text and social media processing, truly exemplifying the characteristics of the ACM SIGMM Award for Outstanding Technical Contributions.