Papers with Code 아카이브

Document Image Classification

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평가표: 8
보존된 평가 기록: 49

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/bcaitech1/p2-klue-Heeseok-Jeong

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/benywon/ReCO

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bfopengradient/NLP_ROBERTA

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/blawok/named-entity-recognition

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bluejurand/Kaggle_QA_Google_Labeling

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/brightmart/roberta_zh

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bshantam97/Attention_Based_Networks

Training data-efficient image transformers & distillation through attention

https://github.com/clovaai/textual-kd-slu

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/code-implementation1/Code1/tree/main/DeiT

Training data-efficient image transformers & distillation through attention

https://github.com/code-implementation1/Code9/tree/main/OCRNet

OCR-free Document Understanding Transformer

https://github.com/cogtoolslab/physics-benchmarking-neurips2021

Training data-efficient image transformers & distillation through attention

https://github.com/common-english/bert-all

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/cydal/LayoutML_pytorch

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

https://github.com/devhemza/BERTweet_sentiment_analysis

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/dig-team/hanna-benchmark-asg

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/doc-analysis/DocBank

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

https://github.com/duanchi1230/NLP_Project_AI2_Reasoning_Challenge

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/eternityyw/tram-benchmark

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/expertailab/spaceqa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/facebookresearch/anli

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/facebookresearch/data2vec_vision

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

https://github.com/facebookresearch/vissl

BEiT: BERT Pre-Training of Image Transformers

https://github.com/few-shot-NER-benchmark/BaselineCode

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/flexible-fl/flex-nlp

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/gatech-eic/vitcod

Training data-efficient image transformers & distillation through attention

https://github.com/haisongzhang/roberta-tiny-cased

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/hiarindam/document-image-classification-TL-SG

Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks

https://github.com/hkuds/easyrec

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/holdfire/CLS

Training data-efficient image transformers & distillation through attention

https://github.com/holdfire/FAS

Training data-efficient image transformers & distillation through attention

https://github.com/huggingface/transformers

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/hustvl/vim

Training data-efficient image transformers & distillation through attention

https://github.com/iamarjunchandra/LayoutLM-Form-Understanding---Sequence-Labeling

Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification

https://github.com/ibm/vira-intent-discovery

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/impira/docquery

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

https://github.com/jacobgil/vit-explain

Training data-efficient image transformers & distillation through attention

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.