Title: Probabilistic Shape for Outlining Objects
High-level object class recognition involves both the task of classification and that of semantic localization (identifying an object's outline). While many recent works focus on classification, the task of localization has received much less attention. In this talk I will present a unified approach for addressing these two tasks in concert, and focus on generic machine learning tasks that arise in this context.
I will first pose object outlining as probabilistic inference based on an explicit shape model. To cope with the computational complexity of this problem, I will develop a template algorithm that significantly improves the performance of an important and popular family of approximate inference methods. I will show how our inference-based approach achieves classification rates that are competitive with state-of-the-art methods while at the same time provides us with accurate outlines that are semantically meaningful.
I will then consider the challenges of learning with weaker supervision and with few samples. I will present an approach that builds on the tools used for object outlining to learn semantic shape models from simple outlines, and show that the models learned are competitive with those learned with full supervision. Finally, I will present a general purpose hierarchical framework for transfer learning between joint distribution representations of related classes. I will show how this approach allows us to learn better class-specific shape models from few samples by learning jointly from several related quadruped classes.