1.8k citations · 4.1k across the 34 of their papers we have counts for
54 papers
Efficient Video Object Segmentation via Modulated Cross-Attention Memory
Abdelrahman Shaker, Syed Talal Wasim, Martin Danelljan +3
Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation. However, these approaches typically struggle on long videos due t…
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…
Learning Video Instance Segmentation with Recurrent Graph Neural Networks
Joakim Johnander, Emil Brissman, Martin Danelljan +1
Most existing approaches to video instance segmentation comprise multiple modules that are heuristically combined to produce the final output. Formulating a purely learning-based m…
Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU
Shipra Jain, Danda Paudel Pani, Martin Danelljan +1
The state-of-the-art object detection and image classification methods can perform impressively on more than 9k and 10k classes, respectively. In contrast, the number of classes in…
Accurate 3D Object Detection using Energy-Based Models
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
Accurate 3D object detection (3DOD) is crucial for safe navigation of complex environments by autonomous robots. Regressing accurate 3D bounding boxes in cluttered environments bas…
Fast Few-Shot Classification by Few-Iteration Meta-Learning
Ardhendu Shekhar Tripathi, Martin Danelljan, Luc Van Gool +1
Autonomous agents interacting with the real world need to learn new concepts efficiently and reliably. This requires learning in a low-data regime, which is a highly challenging pr…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
Prune Truong, Martin Danelljan, Luc Van Gool +1
The feature correlation layer serves as a key neural network module in numerous computer vision problems that involve dense correspondences between image pairs. It predicts a corre…
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Andreas Lugmayr, Martin Danelljan, Luc Van Gool +1
Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. This fundamental fact is largely ignored by state-of-the-art de…
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
Alexandre Carlier, Martin Danelljan, Alexandre Alahi +1
Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions. However, despite the success of deep learning-based mo…
Video Object Segmentation with Episodic Graph Memory Networks
Xiankai Lu, Wenguan Wang, Martin Danelljan +3
How to make a segmentation model efficiently adapt to a specific video and to online target appearance variations are fundamentally crucial issues in the field of video object segm…
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
Dario Fuoli, Zhiwu Huang, Martin Danelljan +18
This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The chall…