Summary of Dragonfly: Multi-resolution Zoom-in Encoding Enhances Vision-language Models, by Rahul Thapa and Kezhen Chen and Ian Covert and Rahul Chalamala and Ben Athiwaratkun and Shuaiwen Leon Song and James Zou
Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language Models
by Rahul Thapa, Kezhen Chen, Ian Covert, Rahul Chalamala, Ben Athiwaratkun, Shuaiwen Leon Song, James Zou
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 Recent advancements in vision-language models (VLMs) have led to the development of higher resolution processing and multi-crop features for preserving native image details. However, existing vision transformers (ViTs) still struggle to capture fine-grained details from less prominent objects, charts, and embedded text, hindering their performance in certain tasks. To address this limitation, our paper introduces an enhanced ViT that not only preserves the native resolution but also zooms in beyond it and extracts features from a large number of image sub-crops. This modification enables our model to better capture fine-grained details, surpassing current ViTs. We demonstrate the effectiveness of our approach by training a model called Dragonfly, which achieves competitive performance on general-domain tasks like ScienceQA and AI2D, as well as exceling in tasks requiring fine-grained image understanding, such as TextVQA and ChartQA. Among models in the 7-8B parameter range, Dragonfly consistently ranks at the top across ten general-domain benchmarks, outperforming larger or more heavily trained models. Our biomedical model, Dragonfly-Med, sets new benchmarks on several medical tasks, achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how computers can better understand images and written text together. Current computer models are good at recognizing big objects and details, but struggle with smaller or more subtle information like charts and tiny text. The researchers created a new model called Dragonfly that can look closely at many parts of an image to find these hidden details. This helps the model do well on tasks like identifying objects in pictures and reading text from images. In fact, Dragonfly is one of the best models for this type of task. The researchers also used their model to help computers understand medical images better, which can be important for doctors. |
Keywords
» Artificial intelligence » Vit
