S. Roth

Publications158
h-index60
Citations25,128
Highly Influential Citations4,013
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The Cityscapes Dataset for Semantic Urban Scene Understanding
TLDR
This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.
View on IEEE
A Database and Evaluation Methodology for Optical Flow
TLDR
This paper proposes a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms and analyzes the results obtained to date to draw a large number of conclusions.
View on IEEE
MOT16: A Benchmark for Multi-Object Tracking
TLDR
A new release of the MOTChallenge benchmark, which focuses on multiple people tracking, and offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest.
View PDF on arXiv
Playing for Data: Ground Truth from Computer Games
TLDR
It is shown that associations between image patches can be reconstructed from the communication between the game and the graphics hardware, which enables rapid propagation of semantic labels within and across images synthesized by the game, with no access to the source code or the content.
View PDF on arXiv
Secrets of optical flow estimation and their principles
TLDR
It is discovered that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques, and while median filtering of intermediate flow fields during optimization is a key to recent performance gains, it leads to higher energy solutions.
View on IEEE
Fields of Experts: a framework for learning image priors
TLDR
A framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks, developed using a Products-of-Experts framework.
View on IEEE
Fields of Experts
TLDR
The approach provides a practical method for learning high-order Markov random field models with potential functions that extend over large pixel neighborhoods with non-linear functions of many linear filter responses.
View on Springer
Benchmarking Denoising Algorithms with Real Photographs
TLDR
This work develops a methodology for benchmarking denoising techniques on real photographs, and captures pairs of images with different ISO values and appropriately adjusted exposure times, where the nearly noise-free low-ISO image serves as reference.
View on IEEE
Shrinkage Fields for Effective Image Restoration
  • Uwe Schmidt, S. Roth
  • Computer Science
    IEEE Conference on Computer Vision and Pattern…
  • 23 June 2014
TLDR
This work proposes shrinkage fields, a random field-based architecture that combines the image model and the optimization algorithm in a single unit, and demonstrates state-of-the-art restoration results with high levels of computational efficiency, and significant speedup potential through inherent parallelism.
View on IEEE
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
TLDR
With MOTChallenge, the work toward a novel multiple object tracking benchmark aimed to address issues of standardization, and the way toward a unified evaluation framework for a more meaningful quantification of multi-target tracking is described.
View PDF on arXiv
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