14 citations · 33 across the 10 of their papers we have counts for
4 papers · 1 filter
WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences
Yujie Lu, Dongfu Jiang, Wenhu Chen +3
Recent breakthroughs in vision-language models (VLMs) emphasize the necessity of benchmarking human preferences in real-world multimodal interactions. To address this gap, we launc…
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?
Xingyu Fu, Muyu He, Yujie Lu +2
We present a novel task and benchmark for evaluating the ability of text-to-image(T2I) generation models to produce images that align with commonsense in real life, which we call C…
Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)
Michael Saxon, Fatima Jahara, Mahsa Khoshnoodi +3
With advances in the quality of text-to-image (T2I) models has come interest in benchmarking their prompt faithfulness -- the semantic coherence of generated images to the prompts…
Lost in Translation? Translation Errors and Challenges for Fair Assessment of Text-to-Image Models on Multilingual Concepts
Michael Saxon, Yiran Luo, Sharon Levy +3
Benchmarks of the multilingual capabilities of text-to-image (T2I) models compare generated images prompted in a test language to an expected image distribution over a concept set.…