207 citations · 432 across the 37 of their papers we have counts for
26 papers · 1 filter
LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter
Tobias Christian Nauen, Anosh Billimoria, Federico Raue +3
Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the b…
OA-CutMix: Correcting the Label Bias of CutMix
Tobias Christian Nauen, Stanislav Frolov, Federico Raue +2
CutMix has become the de facto standard mixing augmentation, yet its label assignment rests on a flawed assumption: The area of the pasted patch faithfully reflects its semantic co…
TextTeacher: What Can Language Teach About Images?
Tobias Christian Nauen, Stanislav Frolov, Brian Bernhard Moser +3
The platonic representation hypothesis suggests that sufficiently large models converge to a shared representation geometry, even across modalities. Motivated by this, we ask: Can…
When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators
Krzysztof Adamkiewicz, Brian Bernhard Moser, Stanislav Frolov +3
Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following. But do they perform well as synthetic vision data generator…
ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions
Tobias Christian Nauen, Brian Moser, Federico Raue +2
Large-scale image classification datasets exhibit strong compositional biases: objects tend to be centered, appear at characteristic scales, and co-occur with class-specific contex…
A Study in Dataset Distillation for Image Super-Resolution
Tobias Dietz, Brian B. Moser, Tobias Nauen +3
Dataset distillation aims to compress large datasets into compact yet highly informative subsets that preserve the training behavior of the original data. While this concept has ga…
Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based Pruning
Brian B. Moser, Federico Raue, Tobias C. Nauen +2
Dataset distillation has gained significant interest in recent years, yet existing approaches typically distill from the entire dataset, potentially including non-beneficial sample…
Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution
Brian B. Moser, Stanislav Frolov, Tobias C. Nauen +2
Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image generation tasks and have shown unexpected potential in image Super-Resolu…
Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning
Arundhati S. Shanbhag, Brian B. Moser, Tobias C. Nauen +3
Diffusion models, celebrated for their generative capabilities, have recently demonstrated surprising effectiveness in image classification tasks by using Bayes' theorem. Yet, curr…
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift
Sanath Budakegowdanadoddi Nagaraju, Brian Bernhard Moser, Tobias Christian Nauen +3
Transformer-based architectures have recently advanced the image reconstruction quality of super-resolution (SR) models. Yet, their scalability remains limited by quadratic attenti…
Unlocking Dataset Distillation with Diffusion Models
Brian B. Moser, Federico Raue, Sebastian Palacio +2
Dataset distillation seeks to condense datasets into smaller but highly representative synthetic samples. While diffusion models now lead all generative benchmarks, current distill…
Diffusion Models, Image Super-Resolution And Everything: A Survey
Brian B. Moser, Arundhati S. Shanbhag, Federico Raue +3
Diffusion Models (DMs) have disrupted the image Super-Resolution (SR) field and further closed the gap between image quality and human perceptual preferences. They are easy to trai…
Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision Transformers
Tobias Christian Nauen, Sebastian Palacio, Federico Raue +1
Self-attention in Transformers comes with a high computational cost because of their quadratic computational complexity, but their effectiveness in addressing problems in language…
Dynamic Attention-Guided Diffusion for Image Super-Resolution
Brian B. Moser, Stanislav Frolov, Federico Raue +2
Diffusion models in image Super-Resolution (SR) treat all image regions uniformly, which risks compromising the overall image quality by potentially introducing artifacts during de…
DartsReNet: Exploring new RNN cells in ReNet architectures
Brian Moser, Federico Raue, Jörn Hees +1
We present new Recurrent Neural Network (RNN) cells for image classification using a Neural Architecture Search (NAS) approach called DARTS. We are interested in the ReNet architec…
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution
Brian Moser, Stanislav Frolov, Federico Raue +2
This paper presents a novel Diffusion-Wavelet (DiWa) approach for Single-Image Super-Resolution (SISR). It leverages the strengths of Denoising Diffusion Probabilistic Models (DDPM…
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances
Brian Moser, Federico Raue, Stanislav Frolov +3
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that req…
Spatial Transformer Networks for Curriculum Learning
Fatemeh Azimi, Jean-Francois Jacques Nicolas Nies, Sebastian Palacio +3
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 c…
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
Stanislav Frolov, Avneesh Sharma, Jörn Hees +3
Conditional image synthesis from layout has recently attracted much interest. Previous approaches condition the generator on object locations as well as class labels but lack fine-…
Adversarial Text-to-Image Synthesis: A Review
Stanislav Frolov, Tobias Hinz, Federico Raue +2
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way fo…
Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching
Fatemeh Azimi, Stanislav Frolov, Federico Raue +2
One-shot Video Object Segmentation~(VOS) is the task of pixel-wise tracking an object of interest within a video sequence, where the segmentation mask of the first frame is given a…
Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory
Fatemeh Azimi, Benjamin Bischke, Sebastian Palacio +3
Video Object Segmentation (VOS) is an active research area of the visual domain. One of its fundamental sub-tasks is semi-supervised / one-shot learning: given only the segmentatio…
ESResNet: Environmental Sound Classification Based on Visual Domain Models
Andrey Guzhov, Federico Raue, Jörn Hees +1
Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years. However, many of the existing approaches a…
P NP, at least in Visual Question Answering
Shailza Jolly, Sebastian Palacio, Joachim Folz +3
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets. One of the most widely-used of these is the…