Summary of Docvlm: Make Your Vlm An Efficient Reader, by Mor Shpigel Nacson et al.
DocVLM: Make Your VLM an Efficient Reader
by Mor Shpigel Nacson, Aviad Aberdam, Roy Ganz, Elad Ben Avraham, Alona Golts, Yair Kittenplon, Shai Mazor, Ron Litman
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed method, DocVLM, aims to enhance document understanding in Vision-Language Models (VLMs) by integrating an OCR-based modality. This approach employs an OCR encoder to capture textual content and layout, compressing them into a compact set of learned queries incorporated into the VLM. Comprehensive evaluations across leading VLMs show that DocVLM significantly reduces reliance on high-resolution images for document understanding. The method improves performance in various benchmarking datasets, including DUDE and MP-DocVQA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DocVLM is a new way to help computers understand documents better. Currently, some computer models are great at recognizing pictures, but they struggle when it comes to reading text. To solve this problem, researchers developed a system that uses special software to recognize words on a page and then combines those recognized words with the original picture. This helps the model understand the document much better than just using the picture alone. The new method is tested on several different datasets and shows significant improvements over previous methods. |
Keywords
» Artificial intelligence » Encoder
