LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding

• URL: http://arxiv.org/abs/2104.08405v1
• Date: Fri, 16 Apr 2021 23:27:39 GMT
• Title: LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding
• Authors: Te-Lin Wu, Cheng Li, Mingyang Zhang, Tao Chen, Spurthi Amba Hombaiah, Michael Bendersky
• Abstract summary: Document layout comprises both structural and visual (eg. font-sizes) information that is vital but often ignored by machine learning models. We propose a novel layout-aware multimodal hierarchical framework, LAMPreT, to model the blocks and the whole document. We evaluate the proposed model on two layout-aware tasks -- text block filling and image suggestion.
• Score: 17.179384053140236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Document layout comprises both structural and visual (eg. font-sizes) information that is vital but often ignored by machine learning models. The few existing models which do use layout information only consider textual contents, and overlook the existence of contents in other modalities such as images. Additionally, spatial interactions of presented contents in a layout were never really fully exploited. To bridge this gap, we parse a document into content blocks (eg. text, table, image) and propose a novel layout-aware multimodal hierarchical framework, LAMPreT, to model the blocks and the whole document. Our LAMPreT encodes each block with a multimodal transformer in the lower-level and aggregates the block-level representations and connections utilizing a specifically designed transformer at the higher-level. We design hierarchical pretraining objectives where the lower-level model is trained similarly to multimodal grounding models, and the higher-level model is trained with our proposed novel layout-aware objectives. We evaluate the proposed model on two layout-aware tasks -- text block filling and image suggestion and show the effectiveness of our proposed hierarchical architecture as well as pretraining techniques.

Related papers

• GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts [53.568057283934714]
  We propose a VLM-based framework that generates content-aware text logo layouts. We introduce two model techniques to reduce the computation for processing multiple glyph images simultaneously. To support instruction-tuning of out model, we construct two extensive text logo datasets, which are 5x more larger than the existing public dataset.
  arXiv  Detail & Related papers  (2024-11-18T10:04:10Z)
• PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM [58.67882997399021]
  Our research introduces a unified framework for automated graphic layout generation. Our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts. We conduct extensive experiments and achieved state-of-the-art (SOTA) performance on public multi-modal layout generation benchmarks.
  arXiv  Detail & Related papers  (2024-06-05T03:05:52Z)
• LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding [0.0]
  This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents. Existing methods have been developed to enhance document comprehension by incorporating pre-training awareness of images, text, and layout structure. Our experiments demonstrate improvement over the baseline model in various document analysis tasks.
  arXiv  Detail & Related papers  (2024-03-21T09:25:24Z)
• Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding [22.00873805952277]
  WebLM is a multimodal pre-training network designed to address the limitations of solely modeling text and structure modalities of HTML in webpages. We propose several pre-training tasks to model the interaction among text, structure, and image modalities effectively. Empirical results demonstrate that the pre-trained WebLM significantly surpasses previous state-of-the-art pre-trained models across several webpage understanding tasks.
  arXiv  Detail & Related papers  (2024-02-28T11:50:36Z)
• LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models [84.16541551923221]
  We propose a model that treats layout generation as a code generation task to enhance semantic information. We develop a Code Instruct Tuning (CIT) approach comprising three interconnected modules. We attain significant state-of-the-art performance on multiple datasets.
  arXiv  Detail & Related papers  (2023-09-18T06:35:10Z)
• Enhancing Visually-Rich Document Understanding via Layout Structure Modeling [91.07963806829237]
  We propose GraphLM, a novel document understanding model that injects layout knowledge into the model. We evaluate our model on various benchmarks, including FUNSD, XFUND and CORD, and achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2023-08-15T13:53:52Z)
• LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding [7.7514466231699455]
  This paper proposes a novel multi-modal pre-training model, LayoutMask. It can enhance the interactions between text and layout modalities in a unified model. It can achieve state-of-the-art results on a wide variety of VrDU problems.
  arXiv  Detail & Related papers  (2023-05-30T03:56:07Z)
• Unifying Vision, Text, and Layout for Universal Document Processing [105.36490575974028]
  We propose a Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. Our method sets the state-of-the-art on 9 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites.
  arXiv  Detail & Related papers  (2022-12-05T22:14:49Z)
• MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding [53.03978356918377]
  spatial hierarchical relationships between content at different levels of granularity are crucial for document image understanding tasks. Existing methods learn features from either word-level or region-level but fail to consider both simultaneously. We propose MGDoc, a new multi-modal multi-granular pre-training framework that encodes page-level, region-level, and word-level information at the same time.
  arXiv  Detail & Related papers  (2022-11-27T22:47:37Z)

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.