1.Topic: Self-Adaptive Visual Learning
2. Language: English
3. Speaker: Yang Wang is an associate professor in the Department of Computer Science, University of Manitoba. He is also currently on leave and working as the Chief Scientist in Computer Vision, Noah's Ark Lab, Huawei Technologies Canada. He did his PhD from Simon Fraser University, MSc from University of Alberta, and BEng from Harbin Institute of Technology. Before joining UManitoba, he worked as a NSERC postdoc at the University of Illinois at Urbana-Champaign.
演讲嘉宾：Yang Wang, 西蒙·弗雷泽大学博士学位、阿尔伯塔大学硕士、哈尔滨工业大学学士。现为曼尼托巴大学计算机科学系副教授，华为技术加拿大公司诺亚方舟实验室计算机视觉首席专家。在加入曼尼托巴大学之前，曾在伊利诺伊大学香槟分校任NSERC博士后。
4. Abstract: There have been significant advances in computer vision in the past few years. Despite of the success, current computer vision systems are still hard to use or deploy in many real-world scenarios. In particular, current computer vision systems usually learn a generic model. But in real world applications, a single generic model is often not powerful enough to handle the diverse scenarios. In this talk, I will introduce some of our recent work on self-adaptive visual learning. Instead of learning and deploying one generic model, our goal is to learn a model that can effectively adapt itself to different environments during testing. I will present applications from several computer visions, such as crowd counting, anomaly detection, personalized highlight detection, etc.