November 19, 2019

12:00 pm / 1:00 pm

Venue

Clark Hall, Room 110

Clark Hall, Room 110  
Seminar 12:00 PM – 1:00 PM
Lunch 1:00 PM – 1:30 PM

Miroslaw Bober ? ?CNN Architectures for Object Recognition and Visual Search? 

?CNN Architectures for  Object Recognition and Visual Search?

Abstract: Visual search and specific object recognition are long-standing challenges in Computer Vision and Artificial Intelligence. Recently the area has been significantly advanced by deep learning. This talk will focus on the latest CNN architectures for robust recognition and retrieval. We will start with a brief introduction to core concepts and techniques, including interest points, local and global image descriptors, geometric verification and efficient matching for large-scale visual search systems. We will then move to CNN architectures and introduce the REMAP global image descriptor, which won the Google Landmark Retrieval Challenge on Kaggle in 2018. I will also present the core ideas behind the ACTNET: our latest CNN network with a novel activation layer that defines the state-of-the-art for recognition. Finally, we will briefly look at some applications, including catching criminals, augmenting paper (a-book) and precise self-localisation via recognition.