报告题目：Can Deep Learning Learn to Count? on cognitive deficit of the current state of deep learning（深度学习不识数？论深度学习的认知缺陷）
演讲人：Xiaolin Wu (武筱林)，IEEE Fellow
单位：Department of Electrical & Computer Engineering,McMaster University
Abstract：Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills, language or arithmetic. Given successes of deep learning(DL) in tasks of visual intelligence and given the primitivity of number sense, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the connectionist CNN machinery itself. A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also, we investigate, using subitizing as a test bed, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting, pointing to both cognitive deficit of pure DL, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful as visual numerosity represents a minimum level of human intelligence.
Brief biography: Xiaolin Wu, Ph.D. in computer science, University of Calgary, Canada, 1988. Dr. Wu started his academic career in 1988, and has since been on the faculty of Western University, Canada, New York Polytechnic University (NYU Poly), and currently McMaster University, where he is a professor at the Department of Electrical & Computer Engineering and holds the NSERC senior industrial research chair in Digital Cinema. His research interests include image processing, network-aware visual computing and communication, multimedia signal coding, and multiple description coding. He has published over three hundred research papers and holds five patents in these fields. Dr. Wu is an IEEE fellow, a McMaster distinguished engineering professor, a past associated editor of IEEE Transactions on Image Processing and IEEE Transactions on Multimedia, and served on the technical committees of many IEEE international conferences/workshops. Dr. Wu received numerous international awards and honors.
武筱林教授，IEEE Fellow，1982年在武汉大学取得计算机科学专业学士学位；1988年在加拿大卡尔加里大学取得计算机科学博士学位。武筱林教授现任加拿大麦克马斯特大学电子计算机工程系终身教授，麦克马斯特大学卓越工学教授，加拿大国家科学与工程研究会高级工业研究主席，加拿大NSERC-DALSA数字影像首席科学家，中国国家千人计划专家等。曾担任IEEETrans.on IP, IEEE Trans. on Multimedia的副主编。武筱林教授的研究领域包括视觉信号处理、数字多媒体计算与通信、信号量化理论、数据压缩、联合信源与信道编码等。他在国际高水平的学术期刊和会议上发表论文300多篇（Google Scholar引用10800余次），专著1部、国际专利5项，取得多项国际知名且颇具影响的学术与工业技术成果。他的世界著名CALIC算法（Context-based Adaptive Lossless Image Code）是无损信号编码领域国际公认的基准技术。他提出的“分层低复杂度无损多媒体数字信号编解码器” 在MPEG数码影院存档国际标准技术评比中获七个侯选算法的第一名。他曾获得的荣誉和奖项包括：加拿大UWO卓越研究教授；丹麦Monsteds研究奖；芬兰诺基亚国际研究奖；VCIP最佳论文奖。