Carnegie Mellon University
Reception for attendees will be held in CS 150 at 3:30 p.m.
There is a famous tale in computer vision: Once, a graduate student asked the famous computer vision scientist Takeo Kanade: “What are the three most important problems in computer vision?” Takeo replied: “Correspondence, correspondence, correspondence!” Indeed, even for the most commonly applied Convolutional Neural Networks (ConvNets), they are internally learning representations that lead to correspondence across objects or object parts. The way these networks learn is via human annotations on millions of static images. For example, humans will label images as dog, car, etc. However, this is not how we humans learn. The visual system of an infant develops in a dynamic and continuous environment without using semantics until much later in life.
In this talk, Xiaolong will argue that we need to go beyond images and exploit the massive amount of correspondence in videos. In videos, we have millions of pixels linked to each other by time. He will discuss how to learn correspondence from continuous observations in videos without any human supervision. Once the correspondence is given, it can be utilized as supervision in training the ConvNets, eliminating the need for manual labels. Besides supervision, capturing long-range correspondence is also the key to video understanding. The effectiveness of these ideas will be demonstrated on tasks including object recognition, tracking, and action recognition.
Xiaolong Wang is a Ph.D. candidate at The Robotics Institute at Carnegie Mellon University. His research interests focus on computer vision and machine learning. He has collaborated with research labs including Berkeley AI Research, Facebook AI Research, and Allen Institute for Artificial Intelligence. He is the recipient of Facebook Fellowship, Nvidia Fellowship, and Baidu Fellowship.