Understanding how humans and machines learn from few examples remains a fundamental challenge. Humans are remarkably able to grasp a new concept from just few examples, or learn a new skill from just few trials. By contrast, state-of-the-art machine learning techniques typically require thousands of training examples and often break down if the training sample set is too small.
In this talk, I will discuss our efforts towards endowing visual learning systems with few-shot learning ability. Our key insight is that the visual world is well structured and highly predictable not only in feature spaces but also in under-explored model and data spaces. Such structures and regularities enable the systems to learn how to learn new tasks rapidly by reusing previous experiences. I will focus on a few topics to demonstrate how to leverage this idea of learning to learn, or meta-learning, to address a broad range of few-shot learning tasks: meta-learning in model space and task-oriented generative modeling. I will also discuss some ongoing work towards building machines that are able to operate in highly dynamic and open environments, making intelligent and independent decisions based on insufficient information.