Summary of Fopru: Focal Pruning For Efficient Large Vision-language Models, by Lei Jiang et al.
FoPru: Focal Pruning for Efficient Large Vision-Language Models
by Lei Jiang, Weizhe Huang, Tongxuan Liu, Yuting Zeng, Jing Li, Lechao Cheng, Xiaohua Xu
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Large Vision-Language Models (LVLMs) have revolutionized the field of multimodal understanding by allowing powerful Large Language Models (LLMs) to comprehend visual input. Traditionally, LVLMs employ visual encoders like CLIP to convert images into tokens, which are then projected and aligned with textual tokens before being fed into the LLM for inference. While existing LVLMs have achieved impressive results, their inference efficiency remains limited by the large number of visual tokens and potential redundancy among them. To address this issue, we propose Focal Pruning (FoPru), a training-free method that prunes visual tokens based on attention-based token significance derived from the vision encoder. Our approach introduces two alternative pruning strategies: rank strategy and row strategy, which prioritize retaining critical tokens or preserving key information in images, respectively. By reordering the selected tokens to maintain their original positional relationships, FoPru can effectively prune redundant tokens while maintaining high accuracy, leading to significant improvements in inference efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a world where computers can understand both words and pictures like humans do! Large Vision-Language Models (LVLMs) are getting closer to making this happen. These models use special algorithms to combine information from images and text. While they’ve made great progress, there’s still room for improvement. Our team has developed a new way to make these models more efficient without sacrificing accuracy. We call it Focal Pruning (FoPru). FoPru looks at how important each piece of visual information is and removes the unimportant parts. This makes the model work faster while keeping its ability to understand images and text well. By doing this, we can make computers even better at understanding us! |
Keywords
» Artificial intelligence » Attention » Encoder » Inference » Pruning » Token
