17 citations · 24 across the 3 of their papers we have counts for
6 papers · 1 filter
Discrete Point Flow Networks for Efficient Point Cloud Generation
Roman Klokov, Edmond Boyer, Jakob Verbeek
Generative models have proven effective at modeling 3D shapes and their statistical variations. In this paper we investigate their application to point clouds, a 3D shape represent…
Anytime Inference with Distilled Hierarchical Neural Ensembles
Adria Ruiz, Jakob Verbeek
Inference in deep neural networks can be computationally expensive, and networks capable of anytime inference are important in mscenarios where the amount of compute or quantity of…
Hierarchical Scene Coordinate Classification and Regression for Visual Localization
Xiaotian Li, Shuzhe Wang, Yi Zhao +2
Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local d…
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image
Roman Klokov, Jakob Verbeek, Edmond Boyer
We study end-to-end learning strategies for 3D shape inference from images, in particular from a single image. Several approaches in this direction have been investigated that expl…
Adaptative Inference Cost With Convolutional Neural Mixture Models
Adria Ruiz, Jakob Verbeek
Despite the outstanding performance of convolutional neural networks (CNNs) for many vision tasks, the required computational cost during inference is problematic when resources ar…
A robust and efficient video representation for action recognition
Heng Wang, Dan Oneata, Jakob Verbeek +1
This paper introduces a state-of-the-art video representation and applies it to efficient action recognition and detection. We first propose to improve the popular dense trajectory…