https://github.com/daod/inters
공식 코드INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
Papers with Code 아카이브
document understanding
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Document understanding involves document classification, layout analysis, information extraction, and DocQA.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dcgm/biblio-dataset
공식 코드BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata Extraction
https://github.com/dengc2023/longdocurl
공식 코드https://github.com/emanuelevivoli/awesome-comics-understanding
공식 코드One missing piece in Vision and Language: A Survey on Comics Understanding
https://github.com/flagopen/flagembedding
공식 코드https://github.com/fuxiaoliu/documentclip
공식 코드DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents
https://github.com/giannisnik/mpad
공식 코드Message Passing Attention Networks for Document Understanding
https://github.com/gitwzl/motbench
공식 코드https://github.com/guoxy25/Ocean-OCR
공식 코드Ocean-OCR: Towards General OCR Application via a Vision-Language Model
https://github.com/harrytea/tgdoc
공식 코드Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs
https://github.com/herobd/dessurt
공식 코드End-to-end Document Recognition and Understanding with Dessurt
https://github.com/hikopensource/davar-lab-ocr
공식 코드TRIE: End-to-End Text Reading and Information Extraction for Document Understanding
https://github.com/hint-lab/doctrack
공식 코드https://github.com/ibm-aur-nlp/PubLayNet
공식 코드https://github.com/ibm-aur-nlp/PubTabNet
공식 코드https://github.com/jpwang/lilt
공식 코드https://github.com/kasuba-badri-vishal/dhrishtikon
공식 코드DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images
https://github.com/khanhnguyen21006/mia_docvqa
공식 코드DocMIA: Document-Level Membership Inference Attacks against DocVQA Models
https://github.com/laytextllm/laytextllm
공식 코드https://github.com/leitro/machineunlearning-docclassification
공식 코드https://github.com/leitro/selfattnscoring-mpdocvqa
공식 코드Multi-Page Document Visual Question Answering using Self-Attention Scoring Mechanism
https://github.com/leloykun/mmfm-challenge
공식 코드Multimodal Structured Generation: CVPR's 2nd MMFM Challenge Technical Report
https://github.com/line-kite/graphlayoutlm
공식 코드Enhancing Visually-Rich Document Understanding via Layout Structure Modeling
https://github.com/maehcm/do-good
공식 코드Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models
https://github.com/mbzuai-oryx/AIN
공식 코드https://github.com/mbzuai-oryx/CAMEL-Bench
공식 코드https://github.com/mbzuai-oryx/arb
공식 코드https://github.com/microsoft/i-code
공식 코드Unifying Vision, Text, and Layout for Universal Document Processing
https://github.com/microsoft/udop
공식 코드Unifying Vision, Text, and Layout for Universal Document Processing
https://github.com/microsoft/unilm
공식 코드LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/microsoft/unilm/tree/master/layoutxlm
공식 코드LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
https://github.com/microsoft/unilm/tree/master/markuplm
공식 코드MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding
https://github.com/microsoft/unilm/tree/master/xdoc
공식 코드XDoc: Unified Pre-training for Cross-Format Document Understanding
https://github.com/mmrezaee/VRTM
공식 코드A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
https://github.com/mysong7nlper/chatgpt_as_keyphrase_generator
공식 코드https://github.com/naver-ai/cream
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.