1.5k citations · 2.6k across the 50 of their papers we have counts for
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VladVA: Discriminative Fine-tuning of LVLMs
Yassine Ouali, Adrian Bulat, Alexandros Xenos +4
Contrastively-trained Vision-Language Models (VLMs) like CLIP have become the de facto approach for discriminative vision-language representation learning. However, these models ha…
FAM Diffusion: Frequency and Attention Modulation for High-Resolution Image Generation with Stable Diffusion
Haosen Yang, Adrian Bulat, Isma Hadji +4
Diffusion models are proficient at generating high-quality images. They are however effective only when operating at the resolution used during training. Inference at a scaled reso…
CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs
Yassine Ouali, Adrian Bulat, Brais Martinez +1
Despite recent successes, LVLMs or Large Vision Language Models are prone to hallucinating details like objects and their properties or relations, limiting their real-world deploym…
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models
Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos
Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential o…
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation
Mehdi Noroozi, Isma Hadji, Brais Martinez +2
In this paper, we introduce YONOS-SR, a novel stable diffusion-based approach for image super-resolution that yields state-of-the-art results using only a single DDIM step. We prop…