Automatic Understanding of the Visual World

摘要

One of the central problems of artificial intelligence is machine perception, i.e., the ability to understand the visual world based on input from sensors, such as cameras. Computer vision is the area which analyzes visual input. In this talk, I will present recent progress in visual understanding. It is for the most part due to the design of robust visual representations and learned models capturing the variability of the visual world based on state-of-the-art machine learning techniques, including convolutional neural networks. Progress has resulted in technology for a variety of applications. I will present in particular results for human action recognition.