Summary of Tap-vl: Text Layout-aware Pre-training For Enriched Vision-language Models, by Jonathan Fhima et al.
TAP-VL: Text Layout-Aware Pre-training for Enriched Vision-Language Models
by Jonathan Fhima, Elad Ben Avraham, Oren Nuriel, Yair Kittenplon, Roy Ganz, Aviad Aberdam, Ron Litman
First submitted to arxiv on: 7 Nov 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 This research paper introduces a novel approach called TAP-VL that enhances the effectiveness of Vision-Language (VL) models in handling text within images. The challenge is addressed by treating OCR information as a distinct modality and seamlessly integrating it into any VL model. The proposed method employs a lightweight transformer-based OCR module, which is pretrained on unlabeled documents and then fine-tuned for integration with LLM. Initial experiments demonstrate consistent performance improvements when applying TAP-VL to top-performing VL models across scene-text and document-based VL benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to recognize text in a picture. It’s hard! To make it easier, researchers developed new ways to do this. One method uses special tools that help extract the text from the image. Another way is to use super high-quality images that are better at recognizing text. This paper talks about making the first method even better by creating a new tool called TAP-VL. It takes the extracted text and combines it with other text in a special way that helps VL models understand pictures better. |
Keywords
» Artificial intelligence » Transformer
