We present an approach for the supervised online learning of object representations based on a biologically motivated architecture of visual processing. We use the output of a rece...
Abstract. Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchi...
Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric P. Xi...
— We investigate modeling and recognition of object manipulation actions for the purpose of imitation based learning in robotics. To model the process, we are using a combination...
Abstract— This paper proposes a method for learning viewpoint detection models for object categories that facilitate sequential object category recognition and viewpoint planning...
The compositional nature of visual objects significantly limits their representation complexity and renders learning of structured object models tractable. Adopting this modeling ...