Martin Danelljan
Martin Danelljan
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SRFlow: Learning the Super-Resolution Space with Normalizing Flow
ECCV 2020
Spotlight
Normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the low-resolution input.
Andreas Lugmayr
,
Martin Danelljan
,
Luc Van Gool
,
Radu Timofte
Cite
Code
arXiv
Blog Post
Energy-Based Models for Deep Probabilistic Regression
ECCV 2020
A general method for accurate regression by learning the conditional target probability distribution as a deep energy-based model.
Fredrik Gustafsson
,
Martin Danelljan
,
Goutam Bhat
,
Thomas Schön
Cite
Code
arXiv
Video (1 min)
Video (10 min)
Code Tracking
Slides
Video Object Segmentation with Episodic Graph Memory Networks
ECCV 2020
Spotlight
A graph-based memory module for Video Object Segmentation.
Xinkai Lu
,
Wenguan Wang
,
Martin Danelljan
,
Tianfei Zhou
,
Jianbing Shen
,
Luc Van Gool
Cite
Code
arXiv
Know Your Surroundings: Exploiting Scene Information for Object Tracking
ECCV 2020
A tracking architecture that exploits the knowledge about the presence of other objects in the surrounding scene to prevent failure.
Goutam Bhat
,
Martin Danelljan
,
Luc Van Gool
,
Radu Timofte
Cite
Code
arXiv
Video (10 min)
Video (1 min)
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
CVPR 2020
Oral
A unified network architecture for dense correspondences applicable to geometric matching, optical flow and semantic matching.
Prune Truong
,
Martin Danelljan
,
Radu Timofte
Cite
Code
Project
Video
DOI
arXiv
Learning Fast and Robust Target Models for Video Object Segmentation
CVPR 2020
Oral
A light-weight optimization-based target model for fast VOS.
Andreas Robinson
,
Felix Järemo Lawin
,
Martin Danelljan
,
Fahad Shahbaz Khan
,
Michael Felsberg
Cite
Code
Video
DOI
arXiv
Probabilistic Regression for Visual Tracking
CVPR 2020
Proposes a general formulation for probabilistic regression, which is then applied to visual tracking.
Martin Danelljan
,
Luc Van Gool
,
Radu Timofte
Cite
Code
Video
DOI
arXiv
Learning Human-Object Interaction Detection using Interaction Points
CVPR 2020
A fully-convolutional approach that directly detects the interactions between human-object pairs.
Tiancai Wang
,
Tong Yang
,
Martin Danelljan
,
Fahad Shahbaz Khan
,
Xiangyu Zhang
,
Jian Sun
Cite
Code
Video
DOI
arXiv
Learning Discriminative Model Prediction for Tracking
ICCV 2019
Oral
An end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction.
Goutam Bhat
,
Martin Danelljan
,
Luc Van Gool
,
Radu Timofte
Cite
Code
Video
DOI
arXiv
Learning the Model Update for Siamese Trackers
ICCV 2019
Replacing the handcrafted update function in Siamese trackers with a learnable update mechanism.
Lichao Zhang
,
Abel Gonzalez-Garcia
,
Joost van de Weijer
,
Martin Danelljan
,
Fahad Shahbaz Khan
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Code
DOI
arXiv
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