107 citations · 211 across the 14 of their papers we have counts for
11 papers · 1 filter
ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generation
Stanislav Frolov, Brian B. Moser, Sebastian Palacio +1
We present ObjBlur, a novel curriculum learning approach to improve layout-to-image generation models, where the task is to produce realistic images from layouts composed of boxes…
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…
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…
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…
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…
What do Deep Networks Like to See?
Sebastian Palacio, Joachim Folz, Jörn Hees +3
We propose a novel way to measure and understand convolutional neural networks by quantifying the amount of input signal they let in. To do this, an autoencoder (AE) was fine-tuned…