Summary of Enhancing Vision Models For Text-heavy Content Understanding and Interaction, by Adithya Tg et al.
Enhancing Vision Models for Text-Heavy Content Understanding and Interaction
by Adithya TG, Adithya SK, Abhinav R Bharadwaj, Abhiram HA, Surabhi Narayan
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 A novel approach has been proposed to enhance traditional vision models’ capability to comprehend text-heavy visual content with multiple images. This includes dataset preprocessing, fine-tuning using instructional-oriented data, and evaluation. The method also involves building a visual chat application integrating CLIP for image encoding and a model from the Massive Text Embedding Benchmark that considers both textual and visual inputs. The proposed approach has achieved an accuracy of 96.71%. The project aims to increase and enhance the capabilities of vision models in understanding complex visual-textual data, contributing to multimodal AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand a textbook filled with graphs and tables. It’s hard, right? This paper is all about making computer programs better at understanding text-heavy images like those. The idea is to show these programs how to use information from both the image and the surrounding text to get a better understanding of what they’re looking at. To do this, researchers have developed a new approach that includes preparing special datasets, fine-tuning their models, and testing them to see how well they work. They’ve even built a special chat app that can understand both text and images. The goal is to make computers smarter at handling complex visual-textual data. |
Keywords
» Artificial intelligence » Embedding » Fine tuning
