A Survey on Video Temporal Grounding With Multimodal Large Language Model
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Summary
This summary is machine-generated.Multimodal large language models (MLLMs) are advancing video temporal grounding (VTG) for better video understanding. This survey reviews VTG-MLLMs, covering their roles, training, and feature processing.
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.
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