報告題目：Towards X Visual Reasoning
報告人：張含望 博士
單位：新加坡南陽理工大學
報告時間：2018年12月26日（星期三）下午3點
地點：翡翠科教樓A909
Abstract:
For decades, we are interested in detecting objects and classifying them into a fixed vocabulary of lexicon. With the maturity of these low-level vision solutions, we are hunger for a higher-level representation of the visual data, so as to extract visual knowledge rather than merely bags of visual entities, allowing machines to reason about human-level decision-making. In particular, we wish an ``X’’ reasoning, where X means eXplainable and eXplicit. In this talk, I will first review a brief history of today’s prevailing visual detection methods that underpin most today’s multimedia frameworks. Then, I will introduce more challenging vision-language tasks such as visual relationship detections, natural language grounding, and image captioning. Finally, I will share my recent vision in visual reasoning by using visual knowledge graph to break current black-box deep learning models for the above multimodal tasks, especially those require reasoning such as visual Q&A and visual dialog. Through my talk, I hope that we are more ambitious to advance to the new era of multimedia research: knowledge representation + reasoning = AI.
報告人簡介：
Dr. Hanwang Zhang is currently an assistant professor at Nanyang Technological University, Singapore. He was a research scientist at the Department of Computer Science, Columbia University, USA. He has received the B.Eng (Hons.) degree in computer science from Zhejiang University, Hangzhou, China, in 2009, and the Ph.D. degree in computer science from the National University of Singapore in 2014. His research interest includes computer vision, multimedia, and social media. Dr. Zhang is the recipient of the Best Demo runner-up award in ACM MM 2012, the Best Student Paper award in ACM MM 2013, and the Best Paper Honorable Mention in ACM SIGIR 2016，and TOMM best paper award 2018. He is also the winner of Best Ph.D. Thesis Award of School of Computing, National University of Singapore, 2014.
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