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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

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research:deeplearning [2020/10/06 10:19]
Christiane Sommer
research:deeplearning [2020/12/01 11:16]
Dr. Vladimir Golkov
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 +{{linktarget>deeplearning}}
 ====== Deep Learning ====== ====== Deep Learning ======
  
-Deep Learning is a powerful machine learning tool that showed outstanding performance in many fields. One of the greatest successes of Deep Learning has been achieved in large scale object recognition with Convolutional Neural Networks (CNNs). CNNs' main power comes from learning data representations directly from data in a hierarchical layer based structure. 
  
-We apply Convolutional Neural Networks in order to solve computer vision tasks such as optical flow, scene understanding, and develop state-of-the-art methods.+{{:research:deeplearning:ddffnet_cut.png?nolink&700|}}
  
-(The content that was here can be restored and embedded via the blog plugin once it is active)+Deep learning is a powerful machine learning framework that has shown outstanding performance in many fields. The main power of deep learning comes from learning data representations directly from data in a hierarchical layer-based structure. 
 + 
 +We apply deep learning to **computer vision, autonomous driving, biomedicine, time series data, language, and other fields**, and develop novel methods. Among the advanced methods we use and develop are **uncertainty quantification, processing of advanced data structures (sequences, graphs, geometry, high-dimensional data), probabilistic graphical models, reinforcement learning, active learning, domain adaptation, anomaly detection, convolutional networks, recurrent networks, and causality inference.**  
 + 
 +Some of the works from our chair include: [[research:optical_flow_estimation|FlowNet]], [[https://hazirbas.com/projects/fusenet/|FuseNet]], [[research:vslam:dvso|DVSO]]
  
 ==== Contact ==== ==== Contact ====
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 ==== Related publications ==== ==== Related publications ====
 <bibtex> <bibtex>
-<keywords>deeplearning</keywords>+<keywords>deep learning</keywords>
 <bytype>-1</bytype> <bytype>-1</bytype>
 </bibtex> </bibtex>
  

Rechte Seite

Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More