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20232025
most citedAn Introduction to Vision-Language Modeling

37 citations · 47 across the 9 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024

EvalGIM: A Library for Evaluating Generative Image Models

Melissa Hall, Oscar Mañas, Reyhane Askari-Hemmat +14

As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound…

cs.CV2024

Boosting Latent Diffusion with Perceptual Objectives

Tariq Berrada, Pietro Astolfi, Melissa Hall +6

Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produ…

cs.CV2024★ 1 cited

On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models

Tariq Berrada Ifriqi, Pietro Astolfi, Melissa Hall +8

Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of the best performing LDM training recipe…

cs.CV2024★ 3 cited

Consistency-diversity-realism Pareto fronts of conditional image generative models

Pietro Astolfi, Marlene Careil, Melissa Hall +5

Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative models as it would enable their use as…

cs.LG2024★ 37 cited

An Introduction to Vision-Language Modeling

Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay +38

Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guid…

cs.CV2024★ 6 cited

Improving Text-to-Image Consistency via Automatic Prompt Optimization

Oscar Mañas, Pietro Astolfi, Melissa Hall +6

Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic im…

cs.LG2024

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi +4

Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be…