Visual Inference

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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

Self-supervised Augmentation Consistency for Adapting Semantic Segmentationsing with real image noise
Self-supervised Augmentation Consistency for Adapting Semantic Segmentationsing with real image noise

News & Events

  • Honorable Mention for Best Paper GCPR 2021

    Jan-Martin Steitz and Stefan Roth won the Honorable Mention for Best Paper award at the German Conference for Pattern Recognition 2021 for the paper “TxT: Crossmodal end-to-end learning with transformers” (jointly with J. Pfeiffer and I. Gurevych)

Recent Publications

  • NeurIPS 2021

    R. Hesse, S. Schaub-Meyer, and S. Roth, “Fast axiomatic attribution for neural networks.” [coming soon]

  • NeurIPS 2021

    N. Araslanov, S. Schaub-Meyer, and S. Roth, “Dense unsupervised learning for video segmentation.” [coming soon]

  • ICCV 2021

    S. Mahajan and S. Roth, “PixelPyramids: Exact inference models from lossless image pyramids.” [open access] [code]

  • CVPR 2021

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

  • CVPR 2021

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

  • CVPR 2021

    J. Hur and S. Roth, “Self-supervised multi-frame monocular scene flow.” [open access] [code]