InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models
InfiniteVL synergizes linear and sparse attention for efficient unlimited-input vision-language models, achieving 1.7x decoding speedup.
Key Findings
Methodology
InfiniteVL employs a hybrid architecture combining Gated DeltaNet and Full Attention layers. A tailored distillation and fine-tuning strategy optimizes visual perception and computational efficiency. A Long-Sequence Architectural Fine-Tuning strategy transforms dense attention into vision-specific sparse mechanisms.
Key Results
- InfiniteVL-Base matches fundamental multimodal performance of mainstream Transformers with a 1.7x decoding speedup.
- InfiniteVL-Offline achieves 5x prefill acceleration at 256K context, maintaining Transformer-level length generalization.
- InfiniteVL-Online delivers robust streaming perception with 25 FPS real-time throughput.
Significance
This research addresses the computational bottleneck of vision-language models in handling ultra-long multimodal inputs by combining linear and sparse attention, providing an efficient solution for academia and industry.
Technical Contribution
InfiniteVL's innovative architecture design and training strategy break efficiency bottlenecks of existing methods, achieving high efficiency in long-sequence processing while retaining visual detail.
Novelty
First to combine linear and sparse attention in vision-language models, proposing specialized variants for online and offline scenarios.
Limitations
- Performance may degrade in extreme long-sequence scenarios.
- Model may not outperform specialized Transformers in certain tasks.
- Further optimization needed for broader application scenarios.
Future Work
Future work could explore more efficient attention mechanisms, optimize deployment on resource-constrained devices, and extend to more multimodal tasks.
AI Executive Summary
Vision-language models face computational bottlenecks when handling ultra-long multimodal inputs. Existing linear architectures, while maintaining constant computation and memory footprints, struggle with high-frequency visual perception. InfiniteVL offers an efficient solution by synergizing linear and sparse attention. The model employs a hybrid architecture combining Gated DeltaNet and Full Attention layers, optimized through tailored distillation and fine-tuning strategies. Experiments show InfiniteVL matches mainstream Transformer performance in multimodal tasks while significantly improving decoding speed and long-sequence processing capabilities. This research provides an efficient solution for academia and industry, addressing the computational bottleneck in handling ultra-long multimodal inputs.
Deep Analysis
Background
Vision-language models have rapidly evolved from static image-text comprehension to continuous real-world multimodal understanding. However, the standard Transformer architecture faces computational and memory bottlenecks when handling long sequences, limiting deployment on resource-constrained devices.
Core Problem
Vision-language models face computational bottlenecks when handling ultra-long multimodal inputs, particularly in high-frequency visual perception tasks where linear architectures significantly degrade in performance.
Innovation
InfiniteVL offers an efficient solution by synergizing linear and sparse attention. Its hybrid architecture combines Gated DeltaNet and Full Attention layers, optimized through tailored distillation and fine-tuning strategies.
Methodology
- �� InfiniteVL-Base employs a hybrid architecture combining Gated DeltaNet and Full Attention layers.
- �� Optimized through tailored distillation and fine-tuning strategies.
- �� Long-Sequence Architectural Fine-Tuning strategy transforms dense attention into vision-specific sparse mechanisms.
Experiments
Experiments utilize multiple datasets, including Video-MME and LongVideoBench, to evaluate the model's performance in long-sequence processing and real-time streaming perception.
Results
InfiniteVL-Base matches mainstream Transformer performance in multimodal tasks, achieving 1.7x decoding speedup. InfiniteVL-Offline achieves 5x prefill acceleration at 256K context, maintaining Transformer-level length generalization.
Applications
InfiniteVL is suitable for scenarios requiring ultra-long multimodal input processing, such as video analysis and real-time streaming perception, enabling efficient deployment on resource-constrained devices.
Limitations & Outlook
Performance may degrade in extreme long-sequence scenarios. Model may not outperform specialized Transformers in certain tasks. Further optimization needed for broader application scenarios.
Plain Language Accessible to non-experts
Imagine a kitchen where the chef needs to handle multiple complex dishes simultaneously. Traditional methods are like a chef using only one pot, inefficiently. InfiniteVL is like a smart kitchen, combining multiple efficient tools to help the chef quickly handle complex tasks. Through intelligent scheduling and optimization, the chef can complete more dishes in less time.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super complex game where you need to handle lots of tasks at once. Traditional methods are like using just one controller, inefficiently. InfiniteVL is like a super game controller, combining multiple efficient tools to help you quickly handle complex tasks. This way, you can complete more tasks and win the game in no time!
Glossary
Linear Attention
An attention mechanism with linear computational complexity, suitable for long-sequence processing.
Used to optimize computational efficiency in vision-language models.
Sparse Attention
An attention mechanism that selectively focuses on important information, reducing computation.
Used to optimize visual detail retention.
Gated DeltaNet
A network structure combining linear and sparse attention.
Used to achieve efficient vision-language models.
Long-Sequence Architectural Fine-Tuning
A strategy to optimize long-sequence processing by transforming dense attention into vision-specific sparse mechanisms.
Used to enhance model performance on ultra-long sequences.
Vision-Language Model
A model combining visual and language information for multimodal tasks.
Used to handle complex visual and language inputs.
Open Questions Unanswered questions from this research
- 1 How to further optimize sparse attention mechanisms for broader application scenarios?
- 2 How to maintain model performance in extreme long-sequence scenarios?
- 3 How to achieve more efficient deployment on resource-constrained devices?
Applications
Immediate Applications
Video Analysis
InfiniteVL can be used for real-time video analysis, helping to identify important events.
Streaming Perception
Achieve efficient real-time processing in streaming scenarios, supporting more applications.
Long-term Vision
Smart Device Deployment
Achieve efficient deployment on resource-constrained devices, supporting more smart applications.
Abstract
Vision-Language Models (VLMs) are increasingly tasked with ultra-long multimodal understanding. While linear architectures offer constant computation and memory footprints, they often struggle with high-frequency visual perception compared to standard Transformers. To bridge this gap, we introduce \textbf{InfiniteVL}. We first develop a hybrid base model called \textbf{InfiniteVL-Base} that interleaves a small fraction of Full Attention layers with Gated DeltaNet. Empowered by a tailored distillation and fine-tuning strategy, InfiniteVL-Base matches the fundamental multimodal performance of equivalent Transformers while achieving a \textbf{1.7$\times$} decoding speedup. However, the quadratic complexity of the retained Full Attention inevitably becomes an efficiency bottleneck when scaling to ultra long context. To break this barrier, we propose a novel Long-Sequence Architectural Fine-Tuning strategy that seamlessly transforms the dense attention into vision-specific sparse mechanisms. This yields two specialized variants: \textbf{InfiniteVL-Offline} for offline retrieval and \textbf{InfiniteVL-Online} for online streaming. By eliminating the computation explosion of global attention without sacrificing high-frequency visual recall, InfiniteVL-Offline achieves Transformer-level length generalization with a \textbf{5x} prefill acceleration at 256K context. Concurrently, InfiniteVL-Online delivers robust streaming perception with a constant memory footprint and a real-time throughput of \textbf{25} FPS. Code and models are available at https://github.com/hustvl/InfiniteVL.