Visual Recognition Beyond Supervised Learning

点击翻译按钮获取中文摘要

In the past few years, there has been significant improvement in the performance of various visual recognition tasks, such as image classification, object detection, semantic segmentation, etc. Most of the success is achieved by a "brute-force" fully supervised approach that learns deep neural network models using large-scale datasets with human annotations. However, this brute-force approach has several limitations that prevent the wide adoption of visual recognition technologies in many real-world applications. Learning these models requires access to large-scale labeled data which are often difficult to collect. Standard supervised learning algorithms usually do not take into account of previously learned knowledge and have to learn from scratch for every new task. The learned systems often do not generalize well to new scenarios. The long-term goal of my research program is to develop algorithms that make it far easier to learn visual recognition systems. We would like to be able to learn visual recognition systems from small data without too much human supervision, to learn new tasks quickly by leveraging previously learned knowledge, and to be able to actively explore and adapt to new environments. In other words, the goal is to move beyond supervised learning and move towards building visual recognition systems that are largely self-taught. Towards realizing this long-term goal, I propose several short-term objectives that I aim to achieve during the 5-year time frame of the proposal: (1) developing new techniques for knowledge transfer in visual recognition; (2) developing algorithms for visual recognition with weak supervision; (3) combining visual recognition and active exploration. This project will produce 16 HQP trained at different levels (PhD, MSc, BSc). These HQP will receive research training in computer vision, machine learning (especially deep learning), and software development. These skills are in high demand in Canada. Students leaving this research program will easily find employment in academia or industry. The proposed research will produce new knowledge that has important impact in terms of both fundamental scientific research and practical applications. The proposed research addresses some fundamental research questions in computer vision and machine learning, such as learning from small data, transfer learning, domain adaptation, weakly-supervised learning, active vision, etc. At the same time, this research has tremendous potential to impact how visual recognition is deployed in real-world applications.