Summary of A-vl: Adaptive Attention For Large Vision-language Models, by Junyang Zhang et al.
A-VL: Adaptive Attention for Large Vision-Language Models
by Junyang Zhang, Mu Yuan, Ruiguang Zhong, Puhan Luo, Huiyou Zhan, Ningkang Zhang, Chengchen Hu, Xiangyang Li
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Large Vision-Language Model (LVLM) integrates computer vision and natural language processing techniques, offering substantial application potential. Adaptive attention techniques can dynamically reduce computational redundancy and thus improve efficiency. Our research proposes A-VL, a plug-and-play adaptive attention tailored for LVLM inference, which outperforms existing methods in reducing memory usage and computational load without compromising performance. We develop this approach by observing that LVLMs generate responses from both remote image tokens and local text tokens, with different modalities having different attention patterns. Our method adapts to these patterns by managing attention separately for each modality. Specifically, for visual input, we store the cache of potentially useful information but only compute the most critical parts. For language input, we care more about local information. We evaluate our approach on three vision-language tasks and five datasets, demonstrating its effectiveness in reducing memory requirements and computational load. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers understand pictures and words is proposed by scientists. They developed a system called A-VL that makes this process faster and uses less energy. The system works by looking at how people usually look at pictures and text together. It finds that people focus on different parts of the picture or text depending on what they’re trying to do, like describing an image or answering a question. The system takes these patterns into account when processing information from both pictures and words. This new approach is tested on several tasks and datasets and shows that it can make computers work faster and use less energy without sacrificing accuracy. |
Keywords
» Artificial intelligence » Attention » Inference » Language model » Natural language processing
