Title: Weakly Supervised Object Detection

Weakly supervised learning of object detection is an important
problem in image understanding that still does not have a satisfactory solution. 
In this talk, we address this problem by improving different aspects of the 
standard multiple instance learning based object detection. 
We first present a method that can represent and exploit 
presence of multiple object instances in an image. Second we further improve this
method by imposing similarity among objects of the same class.
Finally we propose a weakly supervised deep detection
architecture that can exploit the power of deep convolutional neural
networks pre-trained on large-scale image-level classification tasks.
Hakan Bilen received his PhD degree in Electrical Engineering in 2013 and spent a year 
as a postdoctoral researcher at the University of Leuven in Belgium. He is currently a postdoctoral
researcher in the University of Oxford since 2015. His research areas include computer vision and 
machine learning.
Webpage: http://www.robots.ox.ac.uk/~hbilen/
Slides : https://drive.google.com/open?id=0B0evBVYO74MEeFNPbHMzekVIdFk