InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models

1. 1Develop a hybrid base model that combines linear and sparse attention mechanisms
2. 2Interleave a small fraction of Full Attention layers with linear layers to improve high-frequency visual perception
3. 3Implement InfiniteVL-Base to achieve constant computation and memory footprints
4. 4Evaluate the performance of InfiniteVL on ultra-long multimodal understanding tasks

AI engineers and researchers working on vision-language models can benefit from InfiniteVL's highly-efficient architecture, which enables ultra-long multimodal understanding

💡 Synergizing linear and sparse attention enables highly-efficient vision-language models with improved high-frequency visual perception

🔍 InfiniteVL: Efficient vision-language models with unlimited input capacity!