Portrait of Xiaofeng Zhang is unavailable

Xiaofeng Zhang

PhD - Université de Montréal
Supervisor
Aaron Courville
Research Topics
Fairness
Generative Models
Website

Publications

Increasing the Utility of Synthetic Images through Chamfer Guidance
Nicola Dall'Asen
Xiaofeng Zhang
Reyhane Askari Hemmat
Melissa Hall
Jakob Verbeek
Adriana Romero Soriano
Michal Drozdzal
Conditional image generative models hold considerable promise to produce infinite amounts of synthetic training data. Yet, recent progress i… (see more)n generation quality has come at the expense of generation diversity, limiting the utility of these models as a source of synthetic training data. Although guidance-based approaches have been introduced to improve the utility of generated data by focusing on quality or diversity, the (implicit or explicit) utility functions oftentimes disregard the potential distribution shift between synthetic and real data. In this work, we introduce Chamfer Guidance: a training-free guidance approach which leverages a handful of real exemplar images to characterize the quality and diversity of synthetic data. We show that by leveraging the proposed Chamfer Guidance, we can boost the diversity of the generations w.r.t. a dataset of real images while maintaining or improving the generation quality on ImageNet-1k and standard geo-diversity benchmarks. Our approach achieves state-of-the-art few-shot performance with as little as 2 exemplar real images, obtaining 96.4\% in terms of precision, and 86.4\% in terms of distributional coverage, which increase to 97.5\% and 92.7\%, respectively, when using 32 real images. We showcase the benefits of the Chamfer Guidance generation by training downstream image classifiers on synthetic data, achieving accuracy boost of up to 15\% for in-distribution over the baselines, and up to 16\% in out-of-distribution. Furthermore, our approach does not require using the unconditional model, and thus obtains a 31\% reduction in FLOPs w.r.t. classifier-free-guidance-based approaches at sampling time.
2025-08-14
ArXiv (preprint)
doi.org
arxiv.org
CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics
Shravan Nayak
Mehar Bhatia
Xiaofeng Zhang
Verena Rieser
Lisa Anne Hendricks
Sjoerd van Steenkiste
Yash Goyal
Karolina Stanczak
Aishwarya Agrawal
The increasing ubiquity of text-to-image (T2I) models as tools for visual content generation raises concerns about their ability to accurate… (see more)ly represent diverse cultural contexts. In this work, we present the first study to systematically quantify the alignment of T2I models and evaluation metrics with respect to both explicit as well as implicit cultural expectations. To this end, we introduce CulturalFrames, a novel benchmark designed for rigorous human evaluation of cultural representation in visual generations. Spanning 10 countries and 5 socio-cultural domains, CulturalFrames comprises 983 prompts, 3637 corresponding images generated by 4 state-of-the-art T2I models, and over 10k detailed human annotations. We find that T2I models not only fail to meet the more challenging implicit expectations but also the less challenging explicit expectations. Across models and countries, cultural expectations are missed an average of 44% of the time. Among these failures, explicit expectations are missed at a surprisingly high average rate of 68%, while implicit expectation failures are also significant, averaging 49%. Furthermore, we demonstrate that existing T2I evaluation metrics correlate poorly with human judgments of cultural alignment, irrespective of their internal reasoning. Collectively, our findings expose critical gaps, providing actionable directions for developing more culturally informed T2I models and evaluation methodologies.
2025-06-10
ArXiv (preprint)
arxiv.org
arxiv.org
CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics
Shravan Nayak
Mehar Bhatia
Xiaofeng Zhang
Verena Rieser
Lisa Anne Hendricks
Sjoerd van Steenkiste
Yash Goyal
Karolina Stanczak
Aishwarya Agrawal
The increasing ubiquity of text-to-image (T2I) models as tools for visual content generation raises concerns about their ability to accurate… (see more)ly represent diverse cultural contexts. In this work, we present the first study to systematically quantify the alignment of T2I models and evaluation metrics with respect to both explicit as well as implicit cultural expectations. To this end, we introduce CulturalFrames, a novel benchmark designed for rigorous human evaluation of cultural representation in visual generations. Spanning 10 countries and 5 socio-cultural domains, CulturalFrames comprises 983 prompts, 3637 corresponding images generated by 4 state-of-the-art T2I models, and over 10k detailed human annotations. We find that T2I models not only fail to meet the more challenging implicit expectations but also the less challenging explicit expectations. Across models and countries, cultural expectations are missed an average of 44% of the time. Among these failures, explicit expectations are missed at a surprisingly high average rate of 68%, while implicit expectation failures are also significant, averaging 49%. Furthermore, we demonstrate that existing T2I evaluation metrics correlate poorly with human judgments of cultural alignment, irrespective of their internal reasoning. Collectively, our findings expose critical gaps, providing actionable directions for developing more culturally informed T2I models and evaluation methodologies.
2025-06-10
ICML.cc/2025/Workshop/MoFA (poster)
doi.org
openreview.net