14 citations · 33 across the 10 of their papers we have counts for
6 papers · 1 filter
VITED: Video Temporal Evidence Distillation
Yujie Lu, Yale Song, William Wang +2
We investigate complex video question answering via chain-of-evidence reasoning -- identifying sequences of temporal spans from multiple relevant parts of the video, together with…
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…
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks
Xinlu Zhang, Yujie Lu, Weizhi Wang +7
Automatically evaluating vision-language tasks is challenging, especially when it comes to reflecting human judgments due to limitations in accounting for fine-grained details. Alt…
Text as Images: Can Multimodal Large Language Models Follow Printed Instructions in Pixels?
Xiujun Li, Yujie Lu, Zhe Gan +3
Recent multimodal large language models (MLLMs) have shown promising instruction following capabilities on vision-language tasks. In this work, we introduce VISUAL MODALITY INSTRUC…