Convolutional Models for Joint Object Categorization and Pose Estimation
This addresses the challenge of integrating view-invariant categorization with pose-specific representations in computer vision, offering a unified deep learning approach for applications like robotics and augmented reality.
The paper tackles the joint problem of object categorization and pose estimation by adapting Convolutional Neural Networks (CNNs) to simultaneously handle both tasks, achieving better than state-of-the-art performance on two large multi-view datasets.
In the task of Object Recognition, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose regression using these approaches has received relatively much less attention. In this paper we show how deep architectures, specifically Convolutional Neural Networks (CNN), can be adapted to the task of simultaneous categorization and pose estimation of objects. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations of CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets. Our models achieve better than state-of-the-art performance on both datasets.