1.8k citations · 4.1k across the 34 of their papers we have counts for
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GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
Prune Truong, Martin Danelljan, Radu Timofte
Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these…
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +18
This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting,…
Unsupervised Learning for Real-World Super-Resolution
Andreas Lugmayr, Martin Danelljan, Radu Timofte
Most current super-resolution methods rely on low and high resolution image pairs to train a network in a fully supervised manner. However, such image pairs are not available in re…
Learning the Model Update for Siamese Trackers
Lichao Zhang, Abel Gonzalez-Garcia, Joost van de Weijer +2
Siamese approaches address the visual tracking problem by extracting an appearance template from the current frame, which is used to localize the target in the next frame. In gener…
Energy-Based Models for Deep Probabilistic Regression
Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat +1
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one parti…
Multi-Modal Fusion for End-to-End RGB-T Tracking
Lichao Zhang, Martin Danelljan, Abel Gonzalez-Garcia +2
We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a…
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictio…
Discriminative Online Learning for Fast Video Object Segmentation
Andreas Robinson, Felix Järemo Lawin, Martin Danelljan +2
We address the highly challenging problem of video object segmentation. Given only the initial mask, the task is to segment the target in the subsequent frames. In order to effecti…
Learning Discriminative Model Prediction for Tracking
Goutam Bhat, Martin Danelljan, Luc Van Gool +1
The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking. In contrast to most other vision problems, trackin…