150 citations · 259 across the 9 of their papers we have counts for
10 papers
MONET: A Massive, Open, Non-redundant and Enriched Text-to-image dataset
Benjamin Aubin, Gonzalo Iñaki Quintana, Onur Tasar +4
Training large text-to-image models requires high-quality, curated datasets with diverse content and detailed captions. Yet the cost and complexity of collecting, filtering, dedupl…
LBM: Latent Bridge Matching for Fast Image-to-Image Translation
Clément Chadebec, Onur Tasar, Sanjeev Sreetharan +1
In this paper, we introduce Latent Bridge Matching (LBM), a new, versatile and scalable method that relies on Bridge Matching in a latent space to achieve fast image-to-image trans…
Controllable Shadow Generation with Single-Step Diffusion Models from Synthetic Data
Onur Tasar, Clément Chadebec, Benjamin Aubin
Realistic shadow generation is a critical component for high-quality image compositing and visual effects, yet existing methods suffer from certain limitations: Physics-based appro…
Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation
Clément Chadebec, Onur Tasar, Eyal Benaroche +1
In this paper, we propose an efficient, fast, and versatile distillation method to accelerate the generation of pre-trained diffusion models: Flash Diffusion. The method reaches st…
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
Onur Tasar, Alain Giros, Yuliya Tarabalka +2
The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting la…
StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization
Onur Tasar, Yuliya Tarabalka, Alain Giros +2
Domain adaptation for semantic segmentation has recently been actively studied to increase the generalization capabilities of deep learning models. The vast majority of the domain…
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data
Onur Tasar, S L Happy, Yuliya Tarabalka +1
Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorat…
ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks
Onur Tasar, S L Happy, Yuliya Tarabalka +1
Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite…
Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data
Onur Tasar, Yuliya Tarabalka, Pierre Alliez
In spite of remarkable success of the convolutional neural networks on semantic segmentation, they suffer from catastrophic forgetting: a significant performance drop for the alrea…
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis
Baris Gecer, Ozge Yalcinkaya, Onur Tasar +1
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including brea…