🎬 From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding

September 12, 2025 · View on GitHub

📚 A Comprehensive Survey on MultiModal Large Language Models for Long Video Understanding

📋 Table of Contents

🎯 Overview
🔍 Abstract
🌟 Key Contributions
📊 Survey Scope
🤖 Long Video Understanding Models
📈 Benchmarks & Datasets
📊 Performance Analysis
🔬 Technical Analysis
🚀 Future Directions
📚 Citation
🤝 Contributing
📄 License

This repository contains the most comprehensive, up-to-date, and innovative survey on MultiModal Large Language Models (MM-LLMs) for Long Video Understanding. As video content continues to grow exponentially, understanding videos that span from seconds to hours becomes increasingly crucial for various applications including video analysis, content moderation, educational technology, and entertainment.

🎥 Why Long Video Understanding Matters

Scale Challenge: Modern videos range from short clips to multi-hour content
Temporal Complexity: Long videos contain complex temporal dependencies and narrative structures
Real-world Applications: Movie analysis, lecture understanding, surveillance, and documentary processing
Technical Innovation: Pushing the boundaries of multimodal AI capabilities

🚀 What Makes This Survey Unique

📊 Comprehensive Coverage: Systematic review of MultiModal Large Language Models for long video understanding
🎯 Technical Focus: In-depth analysis of model architectures and training methodologies
📈 Benchmark Analysis: Detailed performance comparison across various long video understanding benchmarks
🔬 Research Insights: Analysis of unique challenges in long video understanding
🌐 Academic Rigor: Based on peer-reviewed research and established methodologies

📈 Live Model Performance Tracking

Updated: January 15, 2025

graph TD
    A[Long Video Understanding Tasks] --> B[Video QA]
    A --> C[Temporal Localization]
    A --> D[Video Summarization]
    A --> E[Multi-hour Analysis]
    
    B --> B1[Question Answering]
    B --> B2[Content Understanding]
    
    C --> C1[Event Detection]
    C --> C2[Temporal Grounding]
    
    D --> D1[Key Moment Extraction]
    D --> D2[Narrative Summary]
    
    E --> E1[Long-term Dependencies]
    E --> E2[Cross-temporal Relations]

🔍 Abstract

The integration of Large Language Models (LLMs) with visual encoders has recently shown promising performance in visual understanding tasks, leveraging their inherent capability to comprehend and generate human-like text for visual reasoning. This paper reviews the advancements in MultiModal Large Language Models (MM-LLMs) for long video understanding.

We highlight the unique challenges posed by long videos, including fine-grained spatiotemporal details, dynamic events, and long-term dependencies. We summarize the progress in model design and training methodologies for MM-LLMs understanding long videos and compare their performance on various long video understanding benchmarks. Finally, we discuss future directions for MM-LLMs in long video understanding.

Model	Year	Backbone		Connector			Frame	Token	Training			Long
Model		Visual Encoder	LLMs	Image-level	Video-level	Long-video-level			Hardware	PreT	IT
InstructBLIP	23.05	EVA-CLIP-ViT-G/14	FlanT5, Vicuna-7B/13B	Q-Former	--	--	4	32/128	16 A100-40G	Y-N-N	Y-N-N	No
VideoChat	23.05	EVA-CLIP-ViT-G/14	StableVicuna-13B	Q-Former	Global multi-head relation aggregator	--	8	/32	1 A10	Y-Y-N	Y-Y-N	No
MovieChat	23.07	EVA-CLIP-ViT-G/14	LLama-7B	Q-Former	Frame merging, Q-Former	Merging adjacent frames	2048	32/32	-	E2E	E2E	✅ Yes
TimeChat	23.12	EVA-CLIP-ViT-G/14	LLaMA2-7B	Q-Former	Sliding window Q-Former	Time-aware encoding	96	/96	8 V100-32G	Y-Y-N	N-N-Y	✅ Yes
LONGVILA	24.08	SigLIP-SO400M	Qwen2-1.5B/7B	Multi-Modal Sequence Parallelism			1024	256/	256 A100 80G	Y-Y-N	Y-Y-Y	✅ Yes
NVILA	24.12	SigLIP-SO400M	Qwen2-7B/14B	Spatial-to-Channel Reshaping	Temporal Averaging		256	/8192	128 H100-80G	Y-Y-N	Y-Y-Y	✅ Yes

Benchmark	Videos	Annotations	Avg Duration	Focus
Video-MME	900	2,700	17.0 min	Multi-scale evaluation
VideoVista	-	-	-	Long video understanding
EgoSchema	-	-	180 sec	Egocentric video reasoning
LongVideoBench	-	-	-	Reference-based evaluation
MLVU	-	-	-	Multi-task long video understanding
HourVideo	500	12,976	45.7 min	Hour-level understanding
HLV-1K	1,009	14,847	55.0 min	Comprehensive evaluation
LVBench	103	1,549	68.4 min	Long-form analysis

Reasoning Type	Complexity	Representative Models	Performance Range
Frame-level Events	Low	Most MM-LLMs	85-95%
Short-term Patterns	Medium	Video-LLaVA, TimeChat	75-85%
Long-term Dependencies	High	MovieChat, LongVA	65-80%
Cross-temporal Relations	Very High	LONGVILA, NVILA	60-75%

Strategy	Models	Advantages	Challenges
End-to-End	MovieChat, MA-LMM	Optimal performance	High computational cost
Stage-wise	Video-LLaVA, TimeChat	Stable training	Suboptimal alignment
Hybrid	LongVA, LONGVILA	Balanced approach	Complex implementation