Serge Belongie: Challenges in Fine-Grained Image Analysis

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA is concerned with visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. 

In this talk we review representative examples in the context of recognition, retrieval, and generation/synthesis. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets, related domain-specific applications, and connections with other modalities including text and audio. We conclude by highlighting several research directions and open problems.

Serge Belongie is a professor of Computer Science at the University of Copenhagen, where he also serves as the head of the Pioneer Centre for Artificial Intelligence. Previously, he was a professor of Computer Science at Cornell University, an Associate Dean at Cornell Tech, and a member of the Visiting Faculty program at Google. His research interests include Computer Vision, Machine Learning, Augmented Reality, and Human-in-the-Loop Computing. 

He is also a co-founder of several companies including Digital Persona and Anchovi Labs. He is a recipient of the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review “Innovators Under 35” Award, and the Helmholtz Prize for fundamental contributions in Computer Vision. He is a member of the Royal Danish Academy of Sciences and Letters.