1 citations · 1 across the 5 of their papers we have counts for
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NegT2IBench: When Negation Changes the Picture. A Polarity Benchmark for Text-to-Image Models
Omar Elfatairy, Maria A. Bravo, Jessica Bader +1
Text-to-image (T2I) models are judged by benchmarks that measure whether requested content appears, but these benchmarks largely overlook the complementary ability to satisfy negat…
Stitch: Training-Free Position Control in Multimodal Diffusion Transformers
Jessica Bader, Mateusz Pach, Maria A. Bravo +2
Text-to-Image (T2I) generation models have advanced rapidly in recent years, but accurately capturing spatial relationships like "above" or "to the right of" poses a persistent cha…
SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutions
Jessica Bader, Leander Girrbach, Stephan Alaniz +1
Concept Bottleneck Models (CBMs) and other concept-based interpretable models show great promise for making AI applications more transparent, which is essential in fields like medi…
Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data
Yiwen Liu, Jessica Bader, Jae Myung Kim
With the development of photorealistic diffusion models, models trained in part or fully on synthetic data achieve progressively better results. However, diffusion models still rou…
DataDream: Few-shot Guided Dataset Generation
Jae Myung Kim, Jessica Bader, Stephan Alaniz +2
While text-to-image diffusion models have been shown to achieve state-of-the-art results in image synthesis, they have yet to prove their effectiveness in downstream applications.…