Papers with TempCompass

2 papers
Enhancing Temporal Modeling of Video LLMs via Time Gating (2024.findings-emnlp)

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Challenge: Existing Video Large Language Models neglect temporal information in video data, leading to struggles with temporal-aware video understanding.
Approach: They propose a Time Gating Video LLM (TG-Vid) that employs a time gating module to enhance temporal modeling.
Outcome: The proposed model outperforms existing Large Language Models on video-and-language tasks and ablation studies show that the model outpersforms the existing models.
TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)

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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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