https://github.com/Jordy-VL/multi-modal-early-exit
공식 코드Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting
Papers with Code 아카이브
Document Image Classification
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Document image classification is the task of classifying documents based on images of their contents.
<span style="color:grey; opacity: 0.6">( Image credit: Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-layout
공식 코드ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
https://github.com/PaddlePaddle/VIMER/tree/main/StrucTexT/v2
공식 코드StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
https://github.com/alibaba/AliceMind
공식 코드StructuralLM: Structural Pre-training for Form Understanding
https://github.com/clovaai/donut
공식 코드https://github.com/facebookresearch/deit
공식 코드Training data-efficient image transformers & distillation through attention
https://github.com/jpwang/lilt
공식 코드https://github.com/microsoft/unilm
공식 코드LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/microsoft/unilm/tree/master/beit
공식 코드https://github.com/microsoft/unilm/tree/master/dit
공식 코드DiT: Self-supervised Pre-training for Document Image Transformer
https://github.com/microsoft/unilm/tree/master/layoutlmv3
공식 코드LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/microsoft/unilm/tree/master/layoutxlm
공식 코드LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
https://github.com/pytorch/fairseq
공식 코드https://github.com/soumyaxyz/abstractAnalysis
공식 코드Automatic Recognition of Learning Resource Category in a Digital Library
https://github.com/soumyaxyz/documentclassify
공식 코드Automatic Recognition of Learning Resource Category in a Digital Library
https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rs
공식 코드Revisiting ResNets: Improved Training and Scaling Strategies
https://github.com/thezingaro/multimodal-side-tuning
공식 코드https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News
https://github.com/2023-MindSpore-1/ms-code-163
https://github.com/2023-MindSpore-1/ms-code-2/tree/main/OCRNet
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/beit
https://github.com/2024-MindSpore-1/Code3/tree/main/VI-LayoutXLM
LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
https://github.com/2024-MindSpore-1/Code6/tree/main/beit
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/DeiT
Training data-efficient image transformers & distillation through attention
https://github.com/BordiaS/layoutlm
https://github.com/Burf/VisionTransformer-Tensorflow2
Training data-efficient image transformers & distillation through attention
https://github.com/CalumPerrio/WNUT-2020
https://github.com/E-DEEP/PapersReview
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold
https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data
https://github.com/IMvision12/keras-vision-models
Training data-efficient image transformers & distillation through attention
https://github.com/IndicoDataSolutions/finetune
https://github.com/Karthik-Bhaskar/Context-Based-Question-Answering
https://github.com/MS-P3/code3/tree/main/layoutlm
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/MS-P3/code3/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.