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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?

Yunlong Tang1, Junjia Guo1, Hang Hua1

  • 1University of Rochester.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|September 8, 2025
PubMed
Summary
This summary is machine-generated.

New benchmark VidComposition evaluates Multimodal Large Language Models (MLLMs) on video composition understanding. Current MLLMs struggle with complex visual elements in compiled videos, showing a gap compared to human abilities.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Multimodal Large Language Models (MLLMs) show progress in video analysis.
  • Existing benchmarks lack detailed assessment of video composition understanding in compiled videos.

Purpose of the Study:

  • Introduce VidComposition, a novel benchmark for evaluating MLLMs' video composition understanding.
  • Assess MLLMs' ability to interpret visual element interactions in compiled video contexts.

Main Methods:

  • Curated 982 compiled videos with cinematic-level annotations.
  • Developed 1706 multiple-choice questions covering camera work, narrative, and character elements.
  • Evaluated 33 open-source and proprietary MLLMs.

Main Results:

  • Identified a significant performance gap between MLLMs and human capabilities in video composition understanding.
  • Current MLLMs demonstrate limitations in interpreting complex compiled video structures.

Conclusions:

  • VidComposition benchmark highlights MLLMs' weaknesses in nuanced video comprehension.
  • Findings provide insights for advancing MLLM capabilities in analyzing complex visual narratives.