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cs.CV2025

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…