Portrait of Adriana Romero Soriano is unavailable

Adriana Romero Soriano

Core Industry Member
Canada CIFAR AI Chair
Adjunct professor, McGill University, School of Computer Science
Research Scientist, Meta AI Research (FAIR)
Research Topics
Computer Vision
Deep Learning
Generative Models

Biography

Adriana Romero-Soriano is a research scientist in the Fundamental AI Research (FAIR) team at Meta, adjunct professor at McGill University, core industry member of Mila – Quebec Artificial Intelligence Institute and a Canada CIFAR AI Chair.

Romero-Soriano’s research lies at the intersection of generative models, computer vision and responsible AI, while her most recent work focuses on improving the quality, controllability, consistency and representation diversity of visual content creation systems.

She received her PhD from the University of Barcelona, where she worked with Carlo Gatta, and then spent two years as a postdoctoral researcher at Mila with Yoshua Bengio.

Current Students

PhD - McGill University
Principal supervisor :
PhD - McGill University
Principal supervisor :
Collaborating researcher

Publications

Boosting Latent Diffusion with Perceptual Objectives
Tariq Berrada
Pietro Astolfi
Jakob Verbeek
Melissa Hall
Marton Havasi
Michal Drozdzal
Yohann Benchetrit
Karteek Alahari
On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
Tariq Berrada
Pietro Astolfi
Melissa Hall
Reyhane Askari Hemmat
Yohann Benchetrit
Marton Havasi
Matthew J. Muckley
Karteek Alahari
Jakob Verbeek
Michal Drozdzal
Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, large-scale end-to-e… (see more)nd training of these models is computationally costly, and hence most research focuses either on finetuning pretrained models or experiments at smaller scales. In this work we aim to improve the training efficiency and performance of LDMs with the goal of scaling to larger datasets and higher resolutions. We focus our study on two points that are critical for good performance and efficient training: (i) the mechanisms used for semantic level (\eg a text prompt, or class name) and low-level (crop size, random flip, \etc) conditioning of the model, and (ii) pre-training strategies to transfer representations learned on smaller and lower-resolution datasets to larger ones. The main contributions of our work are the following: we present systematic experimental study of these points, we propose a novel conditioning mechanism that disentangles semantic and low-level conditioning, we obtain state-of-the-art performance on CC12M for text-to-image at 512 resolution.
What makes a good metric? Evaluating automatic metrics for text-to-image consistency
Candace Ross
Melissa Hall
Adina Williams
Decomposed evaluations of geographic disparities in text-to-image models
Abhishek Sureddy
Dishant Padalia
Nandhinee Periyakaruppan
Oindrila Saha
Adina Williams
Megan Richards
Polina Kirichenko
Melissa Hall
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Jack Urbanek
Florian Bordes
Pietro Astolfi
Mary Williamson
Vasu Sharma
Consistency-diversity-realism Pareto fronts of conditional image generative models
Pietro Astolfi
Marlene Careil
Melissa Hall
Oscar Mañas
Matthew J. Muckley
Jakob Verbeek
Michal Drozdzal
Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative… (see more) models as it would enable their use as world simulators. For these models to be successful world models, they should not only excel at image quality and prompt-image consistency but also ensure high representation diversity. However, current research in generative models mostly focuses on creative applications that are predominantly concerned with human preferences of image quality and aesthetics. We note that generative models have inference time mechanisms - or knobs - that allow the control of generation consistency, quality, and diversity. In this paper, we use state-of-the-art text-to-image and image-and-text-to-image models and their knobs to draw consistency-diversity-realism Pareto fronts that provide a holistic view on consistency-diversity-realism multi-objective. Our experiments suggest that realism and consistency can both be improved simultaneously; however there exists a clear tradeoff between realism/consistency and diversity. By looking at Pareto optimal points, we note that earlier models are better at representation diversity and worse in consistency/realism, and more recent models excel in consistency/realism while decreasing significantly the representation diversity. By computing Pareto fronts on a geodiverse dataset, we find that the first version of latent diffusion models tends to perform better than more recent models in all axes of evaluation, and there exist pronounced consistency-diversity-realism disparities between geographical regions. Overall, our analysis clearly shows that there is no best model and the choice of model should be determined by the downstream application. With this analysis, we invite the research community to consider Pareto fronts as an analytical tool to measure progress towards world models.
Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance
Reyhane Askari Hemmat
Melissa Hall
Alicia Sun
Candace Ross
Michal Drozdzal
Towards Geographic Inclusion in the Evaluation of Text-to-Image Models
Melissa Hall
Samuel J. Bell
Candace Ross
Adina Williams
Michal Drozdzal
Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of … (see more)thoroughly evaluating their performance and identifying potential biases. In pursuit of models that generate images that are realistic, diverse, visually appealing, and consistent with the given prompt, researchers and practitioners often turn to automated metrics to facilitate scalable and cost-effective performance profiling. However, commonly-used metrics often fail to account for the full diversity of human preference; often even in-depth human evaluations face challenges with subjectivity, especially as interpretations of evaluation criteria vary across regions and cultures. In this work, we conduct a large, cross-cultural study to study how much annotators in Africa, Europe, and Southeast Asia vary in their perception of geographic representation, visual appeal, and consistency in real and generated images from state-of-the art public APIs. We collect over 65,000 image annotations and 20 survey responses. We contrast human annotations with common automated metrics, finding that human preferences vary notably across geographic location and that current metrics do not fully account for this diversity. For example, annotators in different locations often disagree on whether exaggerated, stereotypical depictions of a region are considered geographically representative. In addition, the utility of automatic evaluations is dependent on assumptions about their set-up, such as the alignment of feature extractors with human perception of object similarity or the definition of"appeal"captured in reference datasets used to ground evaluations. We recommend steps for improved automatic and human evaluations.
Improving Text-to-Image Consistency via Automatic Prompt Optimization
Oscar Mañas
Pietro Astolfi
Melissa Hall
Candace Ross
Jack Urbanek
Adina Williams
Michal Drozdzal
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
Jonathan Lebensold
Maziar Sanjabi
Pietro Astolfi
Kamalika Chaudhuri
Mike Rabbat
Chuan Guo
GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning
Aarash Feizi
Randall Balestriero
Arantxa Casanova
We propose Guided Positive Sampling Self-Supervised Learning (GPS-SSL), a general method to inject a priori knowledge into Self-Supervised L… (see more)earning (SSL) positive samples selection. Current SSL methods leverage Data-Augmentations (DA) for generating positive samples and incorporate prior knowledge - an incorrect, or too weak DA will drastically reduce the quality of the learned representation. GPS-SSL proposes instead to design a metric space where Euclidean distances become a meaningful proxy for semantic relationship. In that space, it is now possible to generate positive samples from nearest neighbor sampling. Any prior knowledge can now be embedded into that metric space independently from the employed DA. From its simplicity, GPS-SSL is applicable to any SSL method, e.g. SimCLR or BYOL. A key benefit of GPS-SSL is in reducing the pressure in tailoring strong DAs. For example GPS-SSL reaches 85.58% on Cifar10 with weak DA while the baseline only reaches 37.51%. We therefore move a step forward towards the goal of making SSL less reliant on DA. We also show that even when using strong DAs, GPS-SSL outperforms the baselines on under-studied domains. We evaluate GPS-SSL along with multiple baseline SSL methods on numerous downstream datasets from different domains when the models use strong or minimal data augmentations. We hope that GPS-SSL will open new avenues in studying how to inject a priori knowledge into SSL in a principled manner.
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
Melissa Hall
Candace Ross
Adina Williams
Nicolas Carion
Michal Drozdzal
The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play conte… (see more)nt creation solutions make it crucial to understand their potential biases. In this work, we introduce three indicators to evaluate the realism, diversity and prompt-generation consistency of text-to-image generative systems when prompted to generate objects from across the world. Our indicators complement qualitative analysis of the broader impact of such systems by enabling automatic and efficient benchmarking of geographic disparities, an important step towards building responsible visual content creation systems. We use our proposed indicators to analyze potential geographic biases in state-of-the-art visual content creation systems and find that: (1) models have less realism and diversity of generations when prompting for Africa and West Asia than Europe, (2) prompting with geographic information comes at a cost to prompt-consistency and diversity of generated images, and (3) models exhibit more region-level disparities for some objects than others. Perhaps most interestingly, our indicators suggest that progress in image generation quality has come at the cost of real-world geographic representation. Our comprehensive evaluation constitutes a crucial step towards ensuring a positive experience of visual content creation for everyone. Code is available at https://github.com/facebookresearch/DIG-In/.