no code implementations • 26 Apr 2024 • Brian B. Moser, Ahmed Anwar, Federico Raue, Stanislav Frolov, Andreas Dengel
This distinction is crucial as it circumvents the need to precisely model real-world degradations, which limits contemporary blind image SR research.
no code implementations • 25 Mar 2024 • Brian B. Moser, Federico Raue, Andreas Dengel
We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model.
no code implementations • 6 Mar 2024 • Brian B. Moser, Federico Raue, Sebastian Palacio, Stanislav Frolov, Andreas Dengel
In response to these limitations, the concept of distilling the information on a dataset into a condensed set of (synthetic) samples, namely a distilled dataset, emerged.
no code implementations • 1 Jan 2024 • Brian B. Moser, Arundhati S. Shanbhag, Federico Raue, Stanislav Frolov, Sebastian Palacio, Andreas Dengel
Diffusion Models (DMs) have disrupted the image Super-Resolution (SR) field and further closed the gap between image quality and human perceptual preferences.
no code implementations • 15 Aug 2023 • Brian B. Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio, Andreas Dengel
To address this, we introduce "You Only Diffuse Areas" (YODA), a dynamic attention-guided diffusion method for image SR. YODA selectively focuses on spatial regions using attention maps derived from the low-resolution image and the current time step in the diffusion process.
1 code implementation • 10 Jul 2023 • Brian B. Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio, Andreas Dengel
This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR).
1 code implementation • 11 Apr 2023 • Brian Moser, Federico Raue, Jörn Hees, Andreas Dengel
We present new Recurrent Neural Network (RNN) cells for image classification using a Neural Architecture Search (NAS) approach called DARTS.
1 code implementation • 4 Apr 2023 • Brian Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio, Andreas Dengel
This paper presents a novel Diffusion-Wavelet (DiWa) approach for Single-Image Super-Resolution (SISR).
no code implementations • 27 Sep 2022 • Brian Moser, Federico Raue, Stanislav Frolov, Jörn Hees, Sebastian Palacio, Andreas Dengel
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area.
1 code implementation • 14 Mar 2022 • Brian Moser, Federico Raue, Jörn Hees, Andreas Dengel
One of our surprising findings is that in most cases we can reduce the amount of training data to 25\%, consequently reducing search time to 25\%, while at the same time maintaining the same accuracy as if training on the full dataset.
no code implementations • 22 Aug 2021 • Fatemeh Azimi, Jean-Francois Jacques Nicolas Nies, Sebastian Palacio, Federico Raue, Jörn Hees, Andreas Dengel
Curriculum learning is a bio-inspired training technique that is widely adopted to machine learning for improved optimization and better training of neural networks regarding the convergence rate or obtained accuracy.
no code implementations • 27 Jun 2021 • Fatemeh Azimi, Federico Raue, Joern Hees, Andreas Dengel
Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve the classifier's performance.
4 code implementations • 24 Jun 2021 • Andrey Guzhov, Federico Raue, Jörn Hees, Andreas Dengel
AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90. 07% on the UrbanSound8K and 97. 15% on the ESC-50 datasets.
Ranked #1 on Environmental Sound Classification on ESC-50
no code implementations • 21 May 2021 • Ricard Durall, Stanislav Frolov, Jörn Hees, Federico Raue, Franz-Josef Pfreundt, Andreas Dengel, Janis Keupe
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks.
1 code implementation • 25 Mar 2021 • Stanislav Frolov, Avneesh Sharma, Jörn Hees, Tushar Karayil, Federico Raue, Andreas Dengel
In this paper, we propose a method for attribute controlled image synthesis from layout which allows to specify the appearance of individual objects without affecting the rest of the image.
no code implementations • 25 Jan 2021 • Stanislav Frolov, Tobias Hinz, Federico Raue, Jörn Hees, Andreas Dengel
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area.
1 code implementation • 10 Oct 2020 • Fatemeh Azimi, Stanislav Frolov, Federico Raue, Joern Hees, Andreas Dengel
In this work, we study an RNN-based architecture and address some of these issues by proposing a hybrid sequence-to-sequence architecture named HS2S, utilizing a dual mask propagation strategy that allows incorporating the information obtained from correspondence matching.
1 code implementation • 25 Apr 2020 • Fatemeh Azimi, Benjamin Bischke, Sebastian Palacio, Federico Raue, Joern Hees, Andreas Dengel
Video Object Segmentation (VOS) is an active research area of the visual domain.
1 code implementation • 15 Apr 2020 • Andrey Guzhov, Federico Raue, Jörn Hees, Andreas Dengel
Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years.
Ranked #5 on Environmental Sound Classification on UrbanSound8K (using extra training data)
1 code implementation • 26 Mar 2020 • Shailza Jolly, Sebastian Palacio, Joachim Folz, Federico Raue, Joern Hees, Andreas Dengel
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets.
no code implementations • 8 Jan 2019 • Philipp Blandfort, Tushar Karayil, Federico Raue, Jörn Hees, Andreas Dengel
In this paper, we run an experiment on movie ratings data, where we analyze the effect on embedding quality caused by several fusion strategies in neural networks.
1 code implementation • CVPR 2018 • Sebastian Palacio, Joachim Folz, Jörn Hees, Federico Raue, Damian Borth, Andreas Dengel
To do this, an autoencoder (AE) was fine-tuned on gradients from a pre-trained classifier with fixed parameters.
Ranked #819 on Image Classification on <h2>oi</h2>
no code implementations • 13 Nov 2015 • Federico Raue, Andreas Dengel, Thomas M. Breuel, Marcus Liwicki
We evaluated the proposed extension in the following scenarios: missing elements in one modality (visual or audio) and missing elements in both modalities (visual and sound).
no code implementations • CVPR 2015 • Wonmin Byeon, Thomas M. Breuel, Federico Raue, Marcus Liwicki
This paper addresses the problem of pixel-level segmentation and classification of scene images with an entirely learning-based approach using Long Short Term Memory (LSTM) recurrent neural networks, which are commonly used for sequence classification.