VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
- URL: http://arxiv.org/abs/2407.12594v1
- Date: Wed, 17 Jul 2024 14:16:46 GMT
- Title: VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
- Authors: Ofir Abramovich, Niv Nayman, Sharon Fogel, Inbal Lavi, Ron Litman, Shahar Tsiper, Royee Tichauer, Srikar Appalaraju, Shai Mazor, R. Manmatha,
- Abstract summary: VisFocus is an OCR-free method designed to better exploit the vision encoder's capacity by coupling it directly with the language prompt.
We pair the architecture enhancements with a novel pre-training task, using language masking on a snippet of the document text fed to the visual encoder.
Our experiments demonstrate that this prompt-guided visual encoding approach significantly improves performance.
- Score: 18.609441902943445
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language models. The text component can either be extracted explicitly with the use of external OCR models in OCR-based approaches, or alternatively, the vision model can be endowed with reading capabilities in OCR-free approaches. Typically, the queries to the model are input exclusively to the language component, necessitating the visual features to encompass the entire document. In this paper, we present VisFocus, an OCR-free method designed to better exploit the vision encoder's capacity by coupling it directly with the language prompt. To do so, we replace the down-sampling layers with layers that receive the input prompt and allow highlighting relevant parts of the document, while disregarding others. We pair the architecture enhancements with a novel pre-training task, using language masking on a snippet of the document text fed to the visual encoder in place of the prompt, to empower the model with focusing capabilities. Consequently, VisFocus learns to allocate its attention to text patches pertinent to the provided prompt. Our experiments demonstrate that this prompt-guided visual encoding approach significantly improves performance, achieving state-of-the-art results on various benchmarks.
Related papers
- Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents [7.358946120326249]
We introduce 'Eclair, a text-extraction tool specifically designed to process a wide range of document types.
Given an image, 'Eclair is able to extract formatted text in reading order, along with bounding boxes and their corresponding semantic classes.
'Eclair achieves state-of-the-art accuracy on this benchmark, outperforming other methods across key metrics.
arXiv Detail & Related papers (2025-02-06T17:07:22Z) - DoPTA: Improving Document Layout Analysis using Patch-Text Alignment [3.3181276611945267]
We present a novel image-text alignment technique specially designed for leveraging the textual information in document images to improve performance on visual tasks.
Our document encoder model DoPTA - trained with this technique demonstrates strong performance on a wide range of document image understanding tasks, without requiring OCR during inference.
DoPTA also sets new state-of-the art results on D4LA, and FUNSD, two challenging document visual analysis benchmarks.
arXiv Detail & Related papers (2024-12-17T13:26:31Z) - See then Tell: Enhancing Key Information Extraction with Vision Grounding [54.061203106565706]
We introduce STNet (See then Tell Net), a novel end-to-end model designed to deliver precise answers with relevant vision grounding.
To enhance the model's seeing capabilities, we collect extensive structured table recognition datasets.
arXiv Detail & Related papers (2024-09-29T06:21:05Z) - UNIT: Unifying Image and Text Recognition in One Vision Encoder [51.140564856352825]
UNIT is a novel training framework aimed at UNifying Image and Text recognition within a single model.
We show that UNIT significantly outperforms existing methods on document-related tasks.
Notably, UNIT retains the original vision encoder architecture, making it cost-free in terms of inference and deployment.
arXiv Detail & Related papers (2024-09-06T08:02:43Z) - Focus Anywhere for Fine-grained Multi-page Document Understanding [24.76897786595502]
This paper proposes Fox, an effective pipeline, hybrid data, and tuning strategy, that catalyzes LVLMs to focus anywhere on single/multi-page documents.
We employ multiple vision vocabularies to extract visual hybrid knowledge for interleaved document pages.
We render cross-vocabulary vision data as the foreground to achieve a full reaction of multiple visual vocabularies and in-document figure understanding.
arXiv Detail & Related papers (2024-05-23T08:15:49Z) - Enhancing Visual Document Understanding with Contrastive Learning in
Large Visual-Language Models [56.76307866160105]
We propose a contrastive learning framework, termed Document Object COntrastive learning (DoCo)
DoCo leverages an auxiliary multimodal encoder to obtain the features of document objects and align them to the visual features generated by the vision encoder of Large Visual-Language Models (LVLMs)
We demonstrate that the proposed DoCo serves as a plug-and-play pre-training method, which can be employed in the pre-training of various LVLMs without inducing any increase in computational complexity during the inference process.
arXiv Detail & Related papers (2024-02-29T10:17:27Z) - UReader: Universal OCR-free Visually-situated Language Understanding
with Multimodal Large Language Model [108.85584502396182]
We propose UReader, a first exploration of universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM)
By leveraging the shallow text recognition ability of the MLLM, we only finetuned 1.2% parameters.
Our single model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks.
arXiv Detail & Related papers (2023-10-08T11:33:09Z) - mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document
Understanding [55.4806974284156]
Document understanding refers to automatically extract, analyze and comprehend information from digital documents, such as a web page.
Existing Multi-model Large Language Models (MLLMs) have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition.
arXiv Detail & Related papers (2023-07-04T11:28:07Z) - PreSTU: Pre-Training for Scene-Text Understanding [49.288302725486226]
We propose PreSTU, a novel pre-training recipe dedicated to scene-text understanding (STU)
PreSTU introduces OCR-aware pre-training objectives that encourage the model to recognize text from an image and connect it to the rest of the image content.
We empirically demonstrate the effectiveness of this pre-training approach on eight visual question answering and four image captioning benchmarks.
arXiv Detail & Related papers (2022-09-12T18:29:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.