no code implementations • 12 Dec 2022 • Oliver Wiedemann, Vlad Hosu, Shaolin Su, Dietmar Saupe
Through KonX, we provide empirical evidence of label shifts caused by changes in the presentation resolution.
no code implementations • 6 Oct 2022 • Mariia Zameshina, Olivier Teytaud, Fabien Teytaud, Vlad Hosu, Nathanael Carraz, Laurent Najman, Markus Wagner
We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling.
no code implementations • 11 Jul 2022 • Shaolin Su, Hanhe Lin, Vlad Hosu, Oliver Wiedemann, Jinqiu Sun, Yu Zhu, Hantao Liu, Yanning Zhang, Dietmar Saupe
An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation.
1 code implementation • 28 Sep 2020 • Baptiste Roziere, Fabien Teytaud, Vlad Hosu, Hanhe Lin, Jeremy Rapin, Mariia Zameshina, Olivier Teytaud
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both.
1 code implementation • 25 Sep 2020 • Baptiste Roziere, Nathanal Carraz Rakotonirina, Vlad Hosu, Andry Rasoanaivo, Hanhe Lin, Camille Couprie, Olivier Teytaud
More generally, our approach can be used to optimize any method based on noise injection.
1 code implementation • 10 Sep 2020 • Franz Götz-Hahn, Vlad Hosu, Dietmar Saupe
Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets.
no code implementations • 9 May 2020 • Franz Götz-Hahn, Vlad Hosu, Dietmar Saupe
In Neural Processing Letters 50, 3 (2019) a machine learning approach to blind video quality assessment was proposed.
1 code implementation • 20 Jan 2020 • Hanhe Lin, Vlad Hosu, Dietmar Saupe
We propose a new IQA dataset and a weakly supervised feature learning approach to train features more suitable for IQA of artificially distorted images.
no code implementations • 10 Jan 2020 • Hui Men, Vlad Hosu, Hanhe Lin, Andrés Bruhn, Dietmar Saupe
This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide the ground truth for designing novel image quality assessment (IQA) methods dedicated to perceptual quality of interpolated images.
1 code implementation • 7 Jan 2020 • Hanhe Lin, Vlad Hosu, Chunling Fan, Yun Zhang, Yuchen Mu, Raouf Hamzaoui, Dietmar Saupe
We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples.
no code implementations • 17 Dec 2019 • Franz Götz-Hahn, Vlad Hosu, Hanhe Lin, Dietmar Saupe
Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos.
2 code implementations • 14 Oct 2019 • Vlad Hosu, Hanhe Lin, Tamas Sziranyi, Dietmar Saupe
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets.
Ranked #16 on Video Quality Assessment on MSU NR VQA Database
1 code implementation • CVPR 2019 • Vlad Hosu, Bastian Goldlucke, Dietmar Saupe
We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database.
Ranked #3 on Aesthetics Quality Assessment on AVA
no code implementations • 16 Jan 2019 • Hui Men, Hanhe Lin, Vlad Hosu, Daniel Maurer, Andres Bruhn, Dietmar Saupe
visual quality of interpolated frames mostly based on optical flow estimation.
1 code implementation • 22 Mar 2018 • Hanhe Lin, Vlad Hosu, Dietmar Saupe
The main challenge in applying state-of-the-art deep learning methods to predict image quality in-the-wild is the relatively small size of existing quality scored datasets.
Ranked #3 on Image Quality Assessment on KonIQ-10k