Stanford University, USA
ImageNet Large Scale Visual Recognition Challenge
The ImageNet Large Scale Visual Recognition Challenge (http://image-net.org/challenges/LSVRC/) is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This year the challenge had a record number of submissions (36 teams submitted 123 challenge entries) and appeared in international media such as the New York Times, MIT Technology Review and CBC/Radio-Canada.
In this talk, I will describe the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. I will discuss some of the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, briefly analyze the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy.