🎬 From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding
📚 A Comprehensive Survey on MultiModal Large Language Models for Long Video Understanding
- 🎯 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.
- 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
- 📊 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
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]
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.
- 🎬 Long Video Challenges: Fine-grained spatiotemporal details, dynamic events, and long-term dependencies
- 🏗️ Model Design: Architectural innovations for extended video processing
- 📚 Training Methodologies: Advanced training strategies for long video understanding
- 📊 Benchmark Analysis: Comprehensive performance comparison across various benchmarks
- 🚀 Future Directions: Emerging trends and research opportunities
- Systematic Review: Comprehensive analysis of MultiModal Large Language Models for long video understanding
- Technical Taxonomy: Classification of model architectures and training methodologies
- Benchmark Evaluation: Performance comparison across various long video understanding benchmarks
- Challenge Analysis: In-depth examination of unique challenges in long video processing
- Architecture Patterns: Analysis of visual encoders, LLMs, and connector designs
- Training Strategies: Review of pre-training and instruction-tuning methodologies
- Efficiency Approaches: Examination of memory optimization and computational efficiency techniques
- Performance Analysis: Detailed comparison of model capabilities across different tasks
- Future Opportunities: Identification of emerging research areas and challenges
- Technical Innovations: Analysis of promising architectural and training innovations
- Application Domains: Exploration of real-world applications and deployment considerations
- Dynamic Vision Tokenization: Any-resolution processing with differential frame pruning (VideoLLaMA-3)
- Memory Bank Evolution: Advanced compression techniques for ultra-long context (MA-LMM series)
- Spatial-Temporal Fusion: Enhanced dual-pathway processing (SlowFast-LLaVA approach)
- Variable-Length Attention: Dynamic compression with self-attention mechanisms (Oryx series)
- Multi-Modal Parallelism: Sequence parallelism for 1K+ frame processing (LONGVILA evolution)
This survey provides a comprehensive review of MultiModal Large Language Models (MM-LLMs) for long video understanding, covering:
- Model Architectures: Analysis of visual encoders, language models, and connector designs
- Training Methodologies: Pre-training and instruction-tuning strategies
- Long Video Challenges: Spatiotemporal details, dynamic events, and long-term dependencies
- Benchmark Evaluation: Performance comparison across various long video understanding tasks
- Future Directions: Emerging research opportunities and technical challenges
timeline
title Evolution of Long Video Understanding Models
2023 Q2 : InstructBLIP (23.05)
: VideoChat (23.05)
: Video-LLaMA (23.06)
: Video-ChatGPT (23.06)
: Valley (23.06)
2023 Q3 : MovieChat (23.07)
2023 Q4 : LLaMA-VID (23.11)
: VideoChat2 (23.11)
: TimeChat (23.12)
2024 Q1 : LongVLM (23.04)
: Momentor (24.02)
: MovieLLM (24.03)
: MA-LMM (24.04)
: ST-LLM (24.04)
2024 Q3 : LONGVILA (24.08)
: Qwen2-VL (24.09)
: Oryx-1.5 (24.10)
2024 Q4 : TimeMarker (24.11)
: NVILA (24.12)
2025 Q1 : VideoChat-Flash (25.01)
: R1-VL (25.03)
🔍 Click to expand the comprehensive model comparison table
| 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 | |
Note: This is a condensed view. The full table contains 50+ models with detailed specifications.
- MovieChat: Sparse memory mechanism for long video processing
- MA-LMM: Memory bank compression for efficient storage
- TimeChat: Time-aware encoding with sliding windows
- LONGVILA: Multi-modal sequence parallelism
- LongVA: Token expansion and compression strategies
- Video-XL: Dynamic compression techniques
- LongVLM: Hierarchical token merging
- SlowFast-LLaVA: Dual-pathway processing
- LongLLaVA: Hybrid Mamba architecture
| 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 |
- Description: Multi-scale video understanding benchmark
- Strengths: Covers short, medium, and long videos
- Tasks: Video QA, temporal reasoning, content understanding
- Links: Project | GitHub | Dataset | Paper
- Description: Hour-level video understanding evaluation
- Strengths: Focus on very long video content
- Tasks: Long-term temporal reasoning, narrative understanding
- Links: Project | GitHub | Dataset | Paper
- Description: Comprehensive hour-level video benchmark
- Strengths: Large-scale annotations, diverse content
- Tasks: Multi-aspect video understanding
- Links: Project | GitHub | Dataset | Paper
- Description: Long video understanding benchmark
- Strengths: High-quality annotations, challenging scenarios
- Tasks: Complex reasoning over extended content
- Links: Project | GitHub | Dataset | Paper
- NVILA: Leading performance on multiple benchmarks
- LONGVILA: Excellent scalability for very long videos
- TimeMarker: Strong temporal understanding capabilities
- 2024 Models: Significant improvements over 2023 baselines
- Scaling Effects: Larger models generally perform better
- Efficiency Trade-offs: Balance between performance and computational cost
- Models with dedicated long-video architectures outperform general-purpose models
- Memory-augmented approaches show consistent improvements
- Multi-scale processing strategies are becoming standard
This survey analyzes how multimodal large language models process long videos through different architectural components:
graph LR
A[Video Input] --> B[Visual Encoder]
A --> C[Temporal Modeling]
A --> D[Language Integration]
B --> B1[Frame Features]
B --> B2[Spatial Attention]
C --> C1[Temporal Attention]
C --> C2[Memory Mechanisms]
D --> D1[Cross-modal Fusion]
D --> D2[Language Generation]
🔍 Key Insights:
- Visual Encoders: Most models use CLIP-based encoders for frame-level feature extraction
- Memory Mechanisms: Critical for maintaining context across long video sequences
- Temporal Modeling: Varies from simple pooling to sophisticated attention mechanisms
| 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% |
flowchart TD
A[Multimodal Input] --> B{Fusion Strategy}
B --> C[Early Fusion]
B --> D[Late Fusion]
B --> E[Hierarchical Fusion]
C --> C1[Feature Concatenation]
C --> C2[Cross-modal Attention]
D --> D1[Independent Processing]
D --> D2[Decision Combination]
E --> E1[Multi-level Integration]
E --> E2[Adaptive Weighting]
Key Findings: Hierarchical fusion strategies show better performance for long video understanding tasks.
📊 Memory-Augmented Models (15+ models)
├── 🎬 Sparse Memory (MovieChat, MA-LMM)
├── 🔄 Sliding Windows (TimeChat, LLaMA-VID)
└── 📈 Dynamic Compression (Video-XL, Oryx-1.5)
🚀 Efficiency Techniques
├── 🔗 Token Merging (LongVLM, Video-LLaVA)
├── 📊 Hierarchical Processing (SlowFast-LLaVA)
├── 🔄 Parallel Processing (LONGVILA)
└── 📈 Adaptive Pooling (PLLaVA, VideoGPT+)
🔧 Connector Types
├── 🤖 Q-Former Based (MovieChat, TimeChat)
├── 🔗 Cross-Attention (Qwen-VL, EVLM)
├── 📊 MLP Projectors (VITA, LLaVA-OneVision)
└── 🧠 Advanced Fusion (Kangaroo, NVILA)
| 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 |
- Sliding Window Attention: Efficient processing of long sequences
- Hierarchical Temporal Fusion: Multi-scale temporal understanding
- Memory-Augmented Architectures: Long-term dependency modeling
- Token Compression: Reducing computational overhead
- Parallel Processing: Leveraging multiple GPUs effectively
- Dynamic Allocation: Adaptive resource management
- Cross-Modal Attention: Better alignment between modalities
- Temporal-Spatial Integration: Comprehensive scene understanding
- Context-Aware Processing: Adaptive to content complexity
Based on emerging trends from recent research, the following developments are expected:
- VideoLLaMA-3: Dynamic vision tokens with differential frame pruning (up to 180 frames)
- LLaVA-Next-Video: Advanced any-resolution vision tokenization
- Qwen2.5-VL: Enhanced multimodal reasoning with extended context windows
- MovieChat-Pro: Advanced memory bank compression for ultra-long videos
- TimeChat-Ultra: Improved time-aware encoding with sliding window mechanisms
- MA-LMM-v2: Next-generation memory-augmented architectures
- LONGVILA: Enhanced multi-modal sequence parallelism (1024+ frames)
- LongVA: Improved token merging with expanded context (55K+ tokens)
- SlowFast-LLaVA: Optimized dual-pathway processing for temporal understanding
- NVILA-Pro: Spatial-to-channel reshaping with temporal averaging (8K+ frames)
- Oryx-2.0: Variable-length self-attention with dynamic compression
- InstructBLIP-Ultra: Enhanced Q-Former architectures for instruction following
Based on current challenges and limitations in long video understanding, several key research directions emerge:
- Hour-long Video Datasets: Current long-video training data is limited to minutes, restricting effective reasoning for hour-long LVU
- Long Video Pre-training: Fine-grained long-video-language training pairs are lacking compared to image- and short-video-language pairs
- Large-scale Instruction-tuning Datasets: Creating large-scale long-video-instruction datasets is essential for comprehensive understanding
- Comprehensive Evaluation: Benchmarks covering frame-level and segment-level reasoning with time and language
- Hour-level Testing: Current benchmarks at minute level fail to test long-term capabilities adequately
- Multimodal Integration: Incorporating audio and language modalities would significantly benefit LVU tasks
- Catastrophic Forgetting: Addressing loss of spatiotemporal details when reasoning with extensive sequential visual information
- Computational Efficiency: Reducing computational requirements for long video processing
- Memory Systems: Better memory systems for maintaining long-term context and preventing catastrophic forgetting
- Scalable Architectures: Designing architectures that scale with video length and complexity
- Domain Adaptation: Adapting models to specific video domains (medical, educational, entertainment)
- Multimodal Integration: Incorporating additional modalities (audio, text, metadata)
- Interactive Systems: Developing systems that can interact with users about video content
- Accessibility: Creating tools to make video content more accessible
- Content Creation: AI-assisted video editing and production
- Recommendation Systems: Personalized content discovery
- Quality Assessment: Automated content evaluation
- Lecture Analysis: Automated educational content processing
- Student Engagement: Understanding learning patterns
- Accessibility: Enhanced content accessibility features
- Medical Imaging: Long-term patient monitoring
- Surgical Analysis: Procedure understanding and training
- Therapy Assessment: Behavioral analysis and intervention
If you find our survey useful in your research, please consider citing:
@article{zou2024seconds,
title={From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding},
author={Zou, Heqing and Luo, Tianze and Xie, Guiyang and Lv, Fengmao and Wang, Guangcong and Chen, Juanyang and Wang, Zhuochen and Zhang, Hansheng and Zhang, Huaijian and others},
journal={arXiv preprint arXiv:2409.18938},
year={2024}
}We welcome contributions to this survey! Here's how you can help:
- Fork the repository
- Create a feature branch (
git checkout -b feature/new-model) - Add your model/benchmark information
- Commit your changes (
git commit -am 'Add new model: ModelName') - Push to the branch (
git push origin feature/new-model) - Create a Pull Request
- Model Additions: Include complete technical specifications
- Benchmark Updates: Provide official performance numbers
- Documentation: Maintain consistent formatting
- References: Include proper citations and links
- New long video understanding models
- Updated benchmark results
- Technical analysis and insights
- Bug fixes and improvements
This project is licensed under the MIT License - see the LICENSE file for details.
📚 From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding
A comprehensive survey on multimodal large language models for long video understanding
