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

Recent Publications

  • CVPR 2021

    N. Araslanov and S. Roth, “Self-supervised augmentation consistency for adapting semantic segmentation.” [coming soon]

  • CVPR 2021

    J. Dong, S. Roth, and B. Schiele, “Learning spatially-variant MAP models for non-blind image deblurring.” [coming soon]

  • CVPR 2021

    J. Hur and S. Roth, “Self-supervised multi-frame monocular scene flow.” [coming soon]

  • NeurIPS 2020

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

  • 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]