Object pose detection and tracking has recently attracted increasing attention due to its wide
applications in many areas, such as autonomous driving, robotics, and augmented reality …

Object pose recovery has gained increasing attention in the computer vision field as it has
become an important problem in rapidly evolving technological areas related to autonomous …

We present a novel one-shot method for object detection and 6 DoF pose estimation, that
does not require training on target objects. At test time, it takes as input a target image and a …

Abstract Automatic International Classification of Diseases (ICD) coding aims to assign
multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is …

This paper proposes a universal framework, called OVE6D, for model-based 6D object pose
estimation from a single depth image and a target object mask. Our model is trained using …

This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D
object from depth information represented by a point cloud. Deep features learned by …

We propose a three-stage 6 DoF object detection method called DPODv2 (Dense Pose
Object Detector) that relies on dense correspondences. We combine a 2D object detector …

Zero-shot learning, the task of learning to recognize new classes not seen during training,
has received considerable attention in the case of 2D image classification. However, despite …

The increasing utilization of medical imaging technology with digital storage capabilities has
facilitated the compilation of large-scale data repositories. Fast access to image samples …

In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can
naturally handle pose-related uncertainties and ambiguities arising in almost all real life …