1.8k citations · 4.1k across the 34 of their papers we have counts for
13 papers · 1 filter
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
Goutam Bhat, Martin Danelljan, Fisher Yu +2
We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learn…
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
Jingyun Liang, Andreas Lugmayr, Kai Zhang +3
Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolu…
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Goutam Bhat, Martin Danelljan, Radu Timofte +25
This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times hig…
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
Lei Ke, Xia Li, Martin Danelljan +3
Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. Most approaches only exploit the temporal dimens…
Learnable Online Graph Representations for 3D Multi-Object Tracking
Jan-Nico Zaech, Dengxin Dai, Alexander Liniger +2
Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality. Most re…
Warp Consistency for Unsupervised Learning of Dense Correspondences
Prune Truong, Martin Danelljan, Fisher Yu +1
The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alterna…
Deep Gaussian Processes for Few-Shot Segmentation
Joakim Johnander, Johan Edstedt, Martin Danelljan +2
Few-shot segmentation is a challenging task, requiring the extraction of a generalizable representation from only a few annotated samples, in order to segment novel query images. A…
Learning Target Candidate Association to Keep Track of What Not to Track
Christoph Mayer, Martin Danelljan, Danda Pani Paudel +1
The presence of objects that are confusingly similar to the tracked target, poses a fundamental challenge in appearance-based visual tracking. Such distractor objects are easily mi…
Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
Bin Zhao, Goutam Bhat, Martin Danelljan +2
Segmenting objects in videos is a fundamental computer vision task. The current deep learning based paradigm offers a powerful, but data-hungry solution. However, current datasets…
Deep Burst Super-Resolution
Goutam Bhat, Martin Danelljan, Luc Van Gool +1
While single-image super-resolution (SISR) has attracted substantial interest in recent years, the proposed approaches are limited to learning image priors in order to add high fre…
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
Valentin Wolf, Andreas Lugmayr, Martin Danelljan +2
The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synt…
Local Memory Attention for Fast Video Semantic Segmentation
Matthieu Paul, Martin Danelljan, Luc Van Gool +1
We propose a novel neural network module that transforms an existing single-frame semantic segmentation model into a video semantic segmentation pipeline. In contrast to prior work…
Learning Accurate Dense Correspondences and When to Trust Them
Prune Truong, Martin Danelljan, Luc Van Gool +1
Establishing dense correspondences between a pair of images is an important and general problem. However, dense flow estimation is often inaccurate in the case of large displacemen…