讲者介绍

李宏扬：北京大学信息科学技术学院在读硕士，本科毕业于北京科技大学，2018年MS COCO Panoptic Segmentation Contest PKU_360团队（第三名）成员之一。目前主要研究兴趣点在于图像中的object detection。希望可以结识更多的人，彼此分享，共同交流。

报告题目：二阶信息是否可以在图像分类中发挥作用？

报告摘要：By stacking deeper layers of convolutions and nonlinearity, convolutional networks (ConvNets) effectively learn from low-level to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate the complex boundaries of thousands of classes in a large-dimensional space, adequate exploration of feature distributions is important for realizing full potentials of ConvNets. However, state-of-the-art works concentrate only on deeper or wider architecture design, while rarely exploring feature statistics higher than first-order. Actually the second order statistics information contains much information that first order information doesn’t have. Therefore the second order information may be helpful for large-scale visual recognition especially for fine-grained classification.

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