迈向可理解的计算机视觉 Issues Towards Comprehensible Computer Vision
讲座名称：
迈向可理解的计算机视觉 Issues Towards Comprehensible Computer Vision
讲座时间：
2020-10-31
讲座人：
陈熙霖
形式：
校区：
兴庆校区
实践学分：
讲座内容：
讲座题目：迈向可理解的计算机视觉
Issues Towards Comprehensible Computer Vision
讲座时间：时间：2020年10月31日上午10点
讲座地点：地点：创新港5号巨构新港报告厅
讲座人：陈熙霖（中科院计算技术研究所）
讲座内容：
In the past decades, computer vision has become the hottest area in artificial intelligence due to it reaches similar or even better results in some typical tasks, such as objects recognition, than human being. However, computer vision is still a far from the goal of automatic understanding scene. To understand scene means the machine should not only know what about an object’s categorization, but also why / how / … / about an object and also their relationship in real world.
In this talk, I will briefly review the history of computer vision, and discuss its tendencies. Some key issues are listed as open problems for next decades. Understandable / explainable will be a crucial feature for open world vision system. Meanwhile, mobility, non-uniform imaging, exploration are all key problems for understandable / explainable computer vision. I will share my points on these relevant problems in this talk. Finally, I will also report some of our preliminary works on these topics.
在过去的几十年里，计算机视觉由于在一些典型的任务中，如物体识别，比人类取得了相似甚至更好的结果，成为人工智能领域中最热门的领域。然而，计算机视觉离自动理解场景的目标还很遥远。理解场景意味着机器不仅要知道一个对象的分类是什么，还应该知道为什么/如何/…/关于一个对象以及它们在现实世界中的关系。
在这篇演讲中，我将简要回顾计算机视觉的历史，并讨论其发展趋势。一些关键问题被列为未来几十年的公开问题。可理解/可解释性将是开放世界视觉系统的一个重要特征。同时，机动性、非均匀成像、探索等都是可理解/可解释计算机视觉的关键问题。在这次演讲中，我将谈谈我对这些相关问题的看法。最后，我将汇报一些我们在这些主题上的基本工作。
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