Visual Inference

Visual Inference

The Visual Inference Lab at TU Darmstadt, led by Prof. Stefan Roth, conducts research in several areas of computer vision with an emphasis on statistical methods and machine learning. We develop mathematical models and algorithms for analyzing and processing digital images with the computer. For example, we work on semantic scene understanding, image motion estimation, deep learning, probabilistic methods, image restoration, and object tracking.

Moreover, we regularly offer courses, seminars and labs in computer science, particularly in computer vision and machine learning.

Recent Highlight

Darmstadt Noise Dataset: Ground truth data for image denoising with real image noise
Darmstadt Noise Dataset: Ground truth data for image denoising with real image noise

News & Events

  • Longuet-Higgins Prize 2020

    June 16, 2020

    Stefan Roth and his co-authors Deqing Sun and Michael J. Black have been awarded the Longuet-Higgins Prize for their CVPR 2010 paper “Secrets of Optical Flow and Their Principles”.

  • Limited Reachability

    Due to the current situation caused by COVID-19, the VISINF team is working from home. We are best reached by email. Stay healthy!

  • Available Bachelor and Master Thesis Topics in Visual Inference Lab

    A detailed list of all available master and bachelor thesis topics are accessible here. Please note that you need to login using your TU-ID and password to be able to view this page.

  • PhD Defense

    January 13, 2020

    Stephan Richter has successfully defended his PhD thesis “Visual Perception with Synthetic Data”. Congratulations!

  • ERC Consolidator Grant

    December 10, 2019

    Stefan Roth has been awarded a Consolidator Grant from the European Research Council (ERC) for his project “RED – Robust, Explainable Deep Networks in Computer Vision.” [press release]

Recent Publications

  • NeurIPS 2020

    J. Dong, S. Roth and B. Schiele, “Deep Wiener deconvolution: Wiener meets deep learning for image deblurring,” oral presentation. [coming soon]

  • WACV 2021

    F. Saeedan, S. Roth, “Boosting Monocular Depth with Panoptic Segmentation Maps.” [coming soon]

  • IJCV

    P. Dendorfer, A. Ošep, A. Milan, K. Schindler, D. Cremers, I. Reid, S. Roth, L. Leal-Taixé, “MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking.” [preprint]

  • NeurIPS 2020

    S. Mahajan and S. Roth, “Diverse image captioning with context-object split latent spaces.” [preprint], [code]

  • CVPR 2020

    A. S. Wannenwetsch and S. Roth, “Probabilistic pixel-adaptive refinement networks.” [preprint], [code]

  • CVPR 2020

    A. Bhattacharyya, S. Mahajan, M. Fritz, B. Schiele, and S. Roth, “Normalizing flows with multi-scale autoregressive priors.” [preprint], [code]

  • CVPR 2020

    J. Hur and S. Roth, “Self-supervised monocular scene flow estimation,” oral presentation. [preprint], [code]