| Challenge: | Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats . |
| Approach: | They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect . |
| Outcome: | The proposed benchmarks show that video large language models exhibit poor temporal perception ability. |
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| Challenge: | Existing video LLMs excel at capturing the overall description of a video but lack the ability to demonstrate an understanding of temporal dynamics and localized content within the video. |
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| Challenge: | Video-language models excel at understanding video content but struggle with spatial relationships, temporal ordering, and cross-frame continuity. |
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| Challenge: | Existing methods to evaluate large language models are limited due to their inherent dynamic nature and the inherent dynamicity of language and information. |
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