Search Results for author: Vlad Hosu

Found 15 papers, 8 papers with code

KonX: Cross-Resolution Image Quality Assessment

no code implementations12 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.

Image Quality Assessment

Fairness in generative modeling

no code implementations6 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.

Fairness

Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model

no code implementations11 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.

Face Image Quality Face Image Quality Assessment +4

EvolGAN: Evolutionary Generative Adversarial Networks

1 code implementation28 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.

Critical analysis on the reproducibility of visual quality assessment using deep features

1 code implementation10 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.

Image Quality Assessment Video Quality Assessment

Comment on "No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features"

no code implementations9 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.

Video Quality Assessment

DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning

1 code implementation20 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.

Multi-Task Learning No-Reference Image Quality Assessment

Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification

no code implementations10 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.

Image Quality Assessment Optical Flow Estimation +1

SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning

1 code implementation7 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.

Image Compression Transfer Learning

KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild

no code implementations17 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.

Transfer Learning Video Quality Assessment +1

Effective Aesthetics Prediction with Multi-level Spatially Pooled Features

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.

Aesthetics Quality Assessment

KonIQ-10k: Towards an ecologically valid and large-scale IQA database

1 code implementation22 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.

Image Quality Assessment valid

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