Abstract

Multi-label image classification is a crucial problem in computer vision, where the goal is to assign multiple labels to one image based on its content. Compared with single-label image classification, multi-label image classification is more general, but it is also more challenging because of the rich semantic information and complex dependency of an image and its labels. In this talk, I will introduce our recent TPAMI work addressing this problem, and share an ongoing work on extending this method for zero-shot learning.

Bio:

Mei-Chen Yeh is an associate professor in the Computer Science and Information Engineering Department, National Taiwan Normal University. She received the B.S. and M.S. degrees in computer science from National Tsing Hua University, and the Ph.D. degree in electrical and computer engineering from the University of California, Santa Barbara. She has been working on multimedia computing and pattern recognition, in particular on the topics of multi-label classification and zero-shot learning.

All faculties and students are welcome to join.