IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning
  in Multimodal LLMs
Existing evaluation frameworks for Multimodal Large Language Models (MLLMs)
primarily focus on image reasoning or general video understanding tasks,
largely overlooking the significant role of image context in video
comprehension. To bridge this gap, we propose IV-Bench, the first comprehensive
benchmark for evaluating Image-Grounded Video Perception and Reasoning.
IV-Bench consists of 967 videos paired with 2,585 meticulously annotated
image-text queries across 13 tasks (7 perception and 6 reasoning tasks) and 5
representative categories. Extensive evaluations of state-of-the-art
open-source (e.g., InternVL2.5, Qwen2.5-VL) and closed-source (e.g., GPT-4o,
Gemini2-Flash and Gemini2-Pro) MLLMs demonstrate that current models
substantially underperform in image-grounded video Perception and Reasoning,
merely achieving at most 28.9% accuracy. Further analysis reveals key factors
influencing model performance on IV-Bench, including inference pattern, frame
number, and resolution. Additionally, through a simple data synthesis approach,
we demonstratethe challenges of IV- Bench extend beyond merely aligning the
data format in the training proecss. These findings collectively provide
valuable insights for future research. Our codes and data are released in
https://github.com/multimodal-art-projection/IV-Bench.
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David Ma
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madehua
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Jarvis Guo
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Zhenlin Wei
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Zhenzhu Yang
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Zhongyuan Peng
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Jun Ma
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Xiao Gu
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Zhoufutu Wen
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Xiaojie Jin
IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning
  in Multimodal LLMs
Existing evaluation frameworks for Multimodal Large Language Models (MLLMs)
primarily focus on image reasoning or general video understanding tasks,
largely overlooking the significant role of image context in video
comprehension. To bridge this gap, we propose IV-Bench, the first comprehensive
benchmark for evaluating Image-Grounded Video Perception and Reasoning.
IV-Bench consists of 967 videos paired with 2,585 meticulously annotated
image-text queries across 13 tasks (7 perception and 6 reasoning tasks) and 5
representative categories. Extensive evaluations of state-of-the-art
open-source (e.g., InternVL2.5, Qwen2.5-VL) and closed-source (e.g., GPT-4o,
Gemini2-Flash and Gemini2-Pro) MLLMs demonstrate that current models
substantially underperform in image-grounded video Perception and Reasoning,
merely achieving at most 28.9% accuracy. Further analysis reveals key factors
influencing model performance on IV-Bench, including inference pattern, frame
number, and resolution. Additionally, through a simple data synthesis approach,
we demonstratethe challenges of IV- Bench extend beyond merely aligning the
data format in the training proecss. These findings collectively provide
valuable insights for future research. Our codes and data are released in
https://github.com/multimodal-art-projection/IV-Bench.
IV-Bench evaluates Image-Grounded Video Perception and Reasoning in MLLMs, revealing substantial underperformance across multiple tasks and factors influencing model accuracy.
Multimodal Large Language Models MLLMs Image-Grounded Video Perception and Reasoning benchmark image-text queries inference pattern frame number resolution data synthesis