17 citations · 34 across the 8 of their papers we have counts for
10 papers
Self-supervised Shape Completion via Involution and Implicit Correspondences
Mengya Liu, Ajad Chhatkuli, Janis Postels +2
3D shape completion is traditionally solved using supervised training or by distribution learning on complete shape examples. Recently self-supervised learning approaches that do n…
3D Compression Using Neural Fields
Janis Postels, Yannick Strümpler, Klara Reichard +2
Neural Fields (NFs) have gained momentum as a tool for compressing various data modalities - e.g. images and videos. This work leverages previous advances and proposes a novel NF-b…
ManiFlow: Implicitly Representing Manifolds with Normalizing Flows
Janis Postels, Martin Danelljan, Luc Van Gool +1
Normalizing Flows (NFs) are flexible explicit generative models that have been shown to accurately model complex real-world data distributions. However, their invertibility constra…
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation
Tao Sun, Mattia Segu, Janis Postels +5
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems. Existing image and video driving datasets, howeve…
Implicit Neural Representations for Image Compression
Yannick Strümpler, Janis Postels, Ren Yang +2
Recently Implicit Neural Representations (INRs) gained attention as a novel and effective representation for various data types. Thus far, prior work mostly focused on optimizing t…
On the Practicality of Deterministic Epistemic Uncertainty
Janis Postels, Mattia Segu, Tao Sun +4
A set of novel approaches for estimating epistemic uncertainty in deep neural networks with a single forward pass has recently emerged as a valid alternative to Bayesian Neural Net…