Summary of Lumos : Empowering Multimodal Llms with Scene Text Recognition, by Ashish Shenoy et al.
Lumos : Empowering Multimodal LLMs with Scene Text Recognition
by Ashish Shenoy, Yichao Lu, Srihari Jayakumar, Debojeet Chatterjee, Mohsen Moslehpour, Pierce Chuang, Abhay Harpale, Vikas Bhardwaj, Di Xu, Shicong Zhao, Longfang Zhao, Ankit Ramchandani, Xin Luna Dong, Anuj Kumar
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 Lumos is an innovative multimodal question-answering system that combines text understanding capabilities with scene text recognition (STR) technology. The STR component extracts text from first-person point-of-view images, which is then used to augment input for a Multimodal Large Language Model (MM-LLM). This architecture addresses numerous challenges related to STR quality, latency, and model inference. We discuss the system design choices, modeling techniques, and comprehensive evaluations of each component, demonstrating high quality and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lumos is a new kind of computer system that can answer questions by understanding both words and pictures. It’s like having a super smart helper that can read and understand what you’re asking! The system uses special technology to recognize text in photos taken from your own point of view, which helps it better understand what you want to know. Building this system was tricky because there were many problems to solve along the way, but we figured out how to make it work really well. |
Keywords
* Artificial intelligence * Inference * Large language model * Question answering
