A Survey on Video Temporal Grounding With Multimodal Large Language Model.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Video temporal grounding (VTG) is crucial for fine-grained video understanding.
• Multimodal large language models (MLLMs) show superior comprehension and reasoning, driving advancements in VTG.
• Existing surveys lack focus on the specific advancements and applications of VTG-MLLMs.

Purpose of the Study:

• To systematically review and categorize current research on VTG-MLLMs.
• To provide a comprehensive overview of the field, addressing a gap in existing literature.
• To analyze the functional roles, training paradigms, and feature processing techniques in VTG-MLLMs.

Main Methods:

• A three-dimensional taxonomy is used: MLLM functional roles, training paradigms, and video feature processing.
• Analysis of architectural significance, temporal reasoning strategies, and spatiotemporal representation effectiveness.
• Discussion of benchmark datasets, evaluation protocols, and empirical findings.

Main Results:

• VTG-MLLMs surpass traditional fine-tuned methods in performance and generalization (zero-shot, multi-task, multi-domain).
• MLLMs' architectural roles, training strategies, and feature processing significantly impact VTG effectiveness.
• Current research demonstrates the growing capabilities and potential of MLLM-driven VTG.

Conclusions:

• VTG-MLLMs represent a significant leap in fine-grained video understanding.
• Further research is needed to address existing limitations and explore promising future directions.
• This survey provides a foundational framework for understanding and advancing VTG-MLLM research.