https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/LayoutLLM
공식 코드LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
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LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
Matching Article Pairs with Graphical Decomposition and Convolutions
Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding
Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution
Bidirectional Context-Aware Hierarchical Attention Network for Document Understanding
Token-level Correlation-guided Compression for Efficient Multimodal Document Understanding
Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting
Hypergraph based Understanding for Document Semantic Entity Recognition
Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction
FRAG: Frame Selection Augmented Generation for Long Video and Long Document Understanding
GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document Understanding
PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Primer AI's Systems for Acronym Identification and Disambiguation
3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding
SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinement
Relation-Rich Visual Document Generator for Visual Information Extraction
MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding
GeoLayoutLM: Geometric Pre-training for Visual Information Extraction
MedICaT: A Dataset of Medical Images, Captions, and Textual References
VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data
On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling
Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding
Understood in Translation, Transformers for Domain Understanding
On Web-based Visual Corpus Construction for Visual Document Understanding
Out-of-Distribution Detection with Attention Head Masking for Multimodal Document Classification
CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.