腾讯会议：693140506
邀请人：张晓岩教授
Abstract：Numerous problems in computer vision and machine learning are inherently discrete. More often, these lead to challenging optimization problems. While convexity is an important property when solving continuous optimization problems, submodularity, often viewed as a discrete analog of convexity, is key to solving many discrete problems. Its characterizing property, diminishing marginal returns, appears naturally in a multitude of settings. Submodularity has long been recognized in combinatorial optimization and game theory, it has seen a recent surge of interest in computer vision, document summarization and video summarization. In this talk, we introduce some of our recent work on submodular functions optimization and its applications on image segmentation and video summarization.
报告人简介：李乔良教授，湖南师范大学数学与统计*女王调教-女王调教视频-女王 调教小说博士生导师、2006年起任中国组合数学与图论专业委员会理事、中国数学会组合数学与图论分委员会委员。主要研究领域包括组合矩阵、图的连通性、随机图、计算机视觉中的组合优化问题等。主持完成国家自然科学基金3项，在研一项。发表论文60余篇。