报告主题：Beyond Scaling: Visual Learning by Adaptation

报告人：Professor Yang Wang

报告时间：2026年7月21日（周二）下午14：00-15：00

报告地址：计算机大楼A411

报告摘要：

There have been significant advances in computer vision in the past decade. Current computer vision systems usually learn a generic model. In order to handle the diversity of the visual world, the current approach is to scale up the model. Although scaling has been proven effective in the era of large language model, I argue that there are also other alternative approaches we should explore. In this talk, I will introduce some of our recent work on building robust computer vision systems via adaptation and continual learning. Instead of learning and deploying one generic model, our goal is to learn a model that can effectively and continuously adapt itself to different environments. I will present applications of this framework in several computer vision applications.

报告人简介：

现任加拿大康考迪亚大学(Concordia University)计算机科学与软件工程系教授。此前，他曾在曼尼托巴大学(University of Manitoba)任教。2020年至2022年期间，他担任华为加拿大消费者业务部计算机视觉首席科学家。他于西蒙菲莎大学(Simon Fraser University)获得博士学位，于阿尔伯塔大学(University of Alberta)获得硕士学位，并于哈尔滨工业大学获得工学学士学位。在加入曼尼托巴大学之前，他曾在伊利诺伊大学厄巴纳-香槟分校 (UIUC)从事NSERC博士后研究工作。他的主要研究方向包括计算机视觉与机器学习。