OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
Paper
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2501.05510
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Published
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44
Datasets:
Tasks:
Video-Text-to-Text
Modalities:
Text
Formats:
webdataset
Languages:
English
Size:
1K - 10K
ArXiv:
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OVO-Bench: How Far is Your Video-LLMs from Real-World Online VideO Understanding?
🔥🔥OVO-Bench is accepted by CVPR 2025!🔥🔥
Important Note: Current codebase is modified compared to our initial arXiv paper. We strongly recommend that any use of OVO-Bench should be based on current edition.
Introduction
🌟 Three distinct problem-solving modes
- Backward Tracing: trace back to past events to answer the question.
- Real-Time Visual Perception: understand and respond to events as they unfold at the current timestamp.
- Forward Active Responding: delay the response until sufficient future information becomes available to answer the question accurately.
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