Towards image compression with perfect realism at ultra-low bitrates
Image codecs are typically optimized to trade-off bitrate vs. distortion metrics. At low bitrates,
this leads to compression artefacts which are easily perceptible, even when training with …
this leads to compression artefacts which are easily perceptible, even when training with …
Learning to generate semantic layouts for higher text-image correspondence in text-to-image synthesis
Existing text-to-image generation approaches have set high standards for photorealism and
text-image correspondence, largely benefiting from web-scale text-image datasets, which …
text-image correspondence, largely benefiting from web-scale text-image datasets, which …
Modulating pretrained diffusion models for multimodal image synthesis
We present multimodal conditioning modules (MCM) for enabling conditional image
synthesis using pretrained diffusion models. Previous multimodal synthesis works rely on …
synthesis using pretrained diffusion models. Previous multimodal synthesis works rely on …
Improving statistical fidelity for neural image compression with implicit local likelihood models
Lossy image compression aims to represent images in as few bits as possible while
maintaining fidelity to the original. Theoretical results indicate that optimizing distortion …
maintaining fidelity to the original. Theoretical results indicate that optimizing distortion …
Diagen: diverse image augmentation with generative models
T Lingenberg, M Reuter, G Sudhakaran… - arxiv preprint arxiv …, 2024 - arxiv.org
Simple data augmentation techniques, such as rotations and flips, are widely used to
enhance the generalization power of computer vision models. However, these techniques …
enhance the generalization power of computer vision models. However, these techniques …
Utilizing greedy nature for multimodal conditional image synthesis in transformers
Multimodal Conditional Image Synthesis (MCIS) aims to generate images according to
different modalities input and their combination, which allows users to describe their …
different modalities input and their combination, which allows users to describe their …
Location-aware adaptive normalization: a deep learning approach for wildfire danger forecasting
Climate change is expected to intensify and increase extreme events in the weather cycle.
Since this has a significant impact on various sectors of our life, recent works are concerned …
Since this has a significant impact on various sectors of our life, recent works are concerned …
medigan: a Python library of pretrained generative models for medical image synthesis
Purpose Deep learning has shown great promise as the backbone of clinical decision
support systems. Synthetic data generated by generative models can enhance the …
support systems. Synthetic data generated by generative models can enhance the …
UNet-like network fused swin transformer and CNN for semantic image synthesis
A Ke, J Luo, B Cai - Scientific Reports, 2024 - nature.com
Semantic image synthesis approaches has been dominated by the modelling of
Convolutional Neural Networks (CNN). Due to the limitations of local perception, their …
Convolutional Neural Networks (CNN). Due to the limitations of local perception, their …
Adversarial supervision makes layout-to-image diffusion models thrive
Despite the recent advances in large-scale diffusion models, little progress has been made
on the layout-to-image (L2I) synthesis task. Current L2I models either suffer from poor …
on the layout-to-image (L2I) synthesis task. Current L2I models either suffer from poor …