Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Semantic classes can be either things (objects with a well-defined shape, eg car, person) or
stuff (amorphous background regions, eg grass, sky). While lots of classification and …

We present DeepVCP-a novel end-to-end learning-based 3D point cloud registration
framework that achieves comparable registration accuracy to prior state-of-the-art geometric …

View-based methods have achieved considerable success in $3 $ D object recognition
tasks. Different from existing view-based methods pooling the view-wise features, we tackle …

Differentiable programming is a fresh programming paradigm which composes
parameterized algorithmic components and optimizes them using gradient search. The …

Abstract Symmetric Positive Definite (SPD) matrix learning methods have become popular in
many image and video processing tasks, thanks to their ability to learn appropriate statistical …

The problem of graph matching under node and pair-wise constraints is fundamental in
areas as diverse as combinatorial optimization, machine learning or computer vision, where …

Accurate 3D human pose estimation from single images is possible with sophisticated deep-
net architectures that have been trained on very large datasets. However, this still leaves …

Learning image similarity metrics in an end-to-end fashion with deep networks has
demonstrated excellent results on tasks such as clustering and retrieval. However, current …

Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized
3D data and high-level structural information on the underlying 3D shapes. As such, it …