October 1, 2025IEEE Transactions on Pattern Analysis and Machine Intelligence

A Survey on Video Temporal Grounding with Multimodal Large Language Model

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VTG-MLLMs outperform traditional methods in competitive performance and generalization across various scenarios.
The survey identifies three key aspects: functional roles of MLLMs, training paradigms, and video feature processing techniques.
Benchmark datasets and evaluation protocols are discussed, along with empirical findings in the field.
Future directions for research are proposed, detailing limitations in current approaches to VTG.

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Implication

This survey examines video temporal grounding methods using multimodal large language models, highlighting their training paradigms and effectiveness.

Cite This Study

Wu et al. (2025) studied this question.

synapsesocial.com/papers/68dd7e78fe798ba2fc496139 https://doi.org/10.1109/tpami.2025.3615586

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Implication

This survey examines video temporal grounding methods using multimodal large language models, highlighting their training paradigms and effectiveness.

A Survey on Video Temporal Grounding with Multimodal Large Language Model

Key Points

Discussion

Implication

Cite This Study

A Survey on Video Temporal Grounding with Multimodal Large Language Model

Key Points

Discussion

Implication

Cite This Study