Summary of Densefusion-1m: Merging Vision Experts For Comprehensive Multimodal Perception, by Xiaotong Li et al.
DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception
by Xiaotong Li, Fan Zhang, Haiwen Diao, Yueze Wang, Xinlong Wang, Ling-Yu Duan
First submitted to arxiv on: 11 Jul 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 The paper introduces Perceptual Fusion, a novel caption engine that addresses the scarcity of high-quality image-text datasets for Multimodal Large Language Models (MLLMs). The engine uses diverse perception experts as image priors to provide explicit information on visual elements and an efficient MLLM as a centric pivot to mimic advanced MLLMs’ perception abilities. The authors generate dense descriptions using DenseFusion-1M, a dataset of 1 million highly representative images from the uncurated LAION dataset. Experimental results show that Perceptual Fusion outperforms existing caption engines, significantly improving the perception and cognition abilities of MLLMs across various vision-language benchmarks, especially with high-resolution inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a better way to describe images so that machines can understand them more accurately. Right now, there aren’t many good datasets for this task, which makes it hard for machines to learn how to do it well. The authors came up with an idea called Perceptual Fusion, which uses lots of different ways of looking at pictures to help machines get better at describing them. They tested their idea and found that it really works! With Perceptual Fusion, machines can understand images much better than before. |
