Papers Document Image Classification
“Document Image Classification” 태그가 달린 논문 52편 · 필터 해제
VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification
Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream task. In this paper, we approach the document cla…
Document ClassificationDocument Image ClassificationLayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
Self-supervised pre-training techniques have achieved remarkable progress in Document AI. Most multimodal pre-trained models use a masked language modeling objective to learn bidirectional representations on the text mod…
cross-modal alignmentDocument AIdocument-image-classificationDocument Image Classification+16DocXClassifier: High Performance Explainable Deep Network for Document Image Classification
Convolutional Neural Networks (ConvNets) have been thoroughly researched for document image classification and are known for their exceptional performance in unimodal image-based document classification. Recently, howe…
ClassificationData AugmentationDocument Classificationdocument-image-classification+5DiT: Self-supervised Pre-training for Document Image Transformer
Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we prop…
Document AIdocument-image-classificationDocument Image ClassificationDocument Layout Analysis+5LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding
Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only …
Document Image Classificationdocument understandingKey Information ExtractionKey-value Pair Extraction+1OCR-free Document Understanding Transformer
Understanding document images (e.g., invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document. Current Visual Document Understanding (…
Document Image Classificationdocument understandingKey-value Pair ExtractionOptical Character Recognition+2Document AI: Benchmarks, Models and Applications
Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. It is an important research direction fo…
Deep LearningDocument AIdocument-image-classificationDocument Image Classification+6Domain Agnostic Few-Shot Learning For Document Intelligence
Few-shot learning aims to generalize to novel classes with only a few samples with class labels. Research in few-shot learning has borrowed techniques from transfer learning, metric learning, meta-learning, and Bayesian …
ClassificationCross-Domain Few-Shotcross-domain few-shot learningDocument Classification+8Efficient Document Image Classification Using Region-Based Graph Neural Network
Document image classification remains a popular research area because it can be commercialized in many enterprise applications across different industries. Recent advancements in large pre-trained computer vision and lan…
ClassificationDocument Classificationdocument-image-classificationDocument Image Classification+3DocFormer: End-to-End Transformer for Document Understanding
We present DocFormer -- a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). VDU is a challenging problem which aims to understand documents in their varied formats (forms, re…
Document Image Classificationdocument understandingBEiT: BERT Pre-Training of Image Transformers
We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose…
Document Image ClassificationDocument Layout Analysisimage-classificationImage Classification+2Toward Automatic Interpretation of 3D Plots
This paper explores the challenge of teaching a machine how to reverse-engineer the grid-marked surfaces used to represent data in 3D surface plots of two-variable functions. These are common in scientific and economic p…
Document Image ClassificationShape from TextureStructuralLM: Structural Pre-training for Form Understanding
Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, while neglecting cell-level layout informati…
document-image-classificationDocument Image ClassificationFormimage-classification+4LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modal…
Document Image Classificationdocument understandingFormKey-value Pair ExtractionRevisiting ResNets: Improved Training and Scaling Strategies
Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the ca…
Action ClassificationDocument Image ClassificationImage ClassificationSemantic Object Interaction Classification+1Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
We address the challenging problem of Natural Language Comprehension beyond plain-text documents by introducing the TILT neural network architecture which simultaneously learns layout information, visual features, and te…
DecoderDocument Image Classificationdocument understandingVisual Question Answering+1LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born docu…
Document Image ClassificationDocument Layout Analysisdocument understandingKey Information Extraction+7Training data-efficient image transformers & distillation through attention
Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images usi…
Document Image ClassificationDocument Layout AnalysisEfficient ViTsFine-Grained Image Classification+2Improving accuracy and speeding up Document Image Classification through parallel systems
This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process …
Document Classificationdocument-image-classificationDocument Image ClassificationGeneral Classification+5Visual and Textual Deep Feature Fusion for Document Image Classification
The topic of text document image classification has been explored extensively over the past few years. Most recent approaches handled this task by jointly learning the visual features of document images and their corresp…
Classificationdocument-image-classificationDocument Image Classificationimage-classification+4