https://github.com/renyang-home/alvc
공식 코드Advancing Learned Video Compression with In-loop Frame Prediction
Papers with Code 아카이브
Document Layout Analysis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 6
- 보존된 평가 기록
- 30
"Document Layout Analysis is performed to determine physical structure of a document, that is, to determine document components. These document components can consist of single connected components-regions [...] of pixels that are adjacent to form single regions [...] , or group of text lines. A text line is a group of characters, symbols, and words that are adjacent, “relatively close” to each other and through which a straight line can be drawn (usually with horizontal or vertical orientation)." L. O'Gorman, "The document spectrum for page layout analysis," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1162-1173, Nov. 1993.
Image credit: PubLayNet: largest dataset ever for document layout analysis
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/s3setewe/sfdla-dladapter
공식 코드https://github.com/samakos/document-ai-
공식 코드https://github.com/shaoyanpan/Synthetic-CT-generation-from-MRI-using-3D-transformer-based-denoising-diffusion-model
공식 코드Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model
https://github.com/shulk97/daniel
공식 코드https://github.com/tanguymagne/uvdoc
공식 코드https://github.com/tensorflow/models
공식 코드Towards End-to-End Unified Scene Text Detection and Layout Analysis
https://github.com/tinyvision/IPCodec
공식 코드Spatiotemporal Entropy Model is All You Need for Learned Video Compression
https://github.com/utlive/enhanced_ssim
공식 코드https://github.com/wyq2021/s2lic
공식 코드https://github.com/zhenxuan-zhang/pta4unpaired_hf_mri_syn
공식 코드Pretext Task Adversarial Learning for Unpaired Low-field to Ultra High-field MRI Synthesis
https://github.com/2023-MindSpore-4/Code2/tree/main/DDAG
CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/beit
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
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/Burf/VisionTransformer-Tensorflow2
Training data-efficient image transformers & distillation through attention
https://github.com/DS4SD/DocLayNet
DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis
https://github.com/DS4SD/docling
DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis
https://github.com/E-DEEP/PapersReview
https://github.com/EgorRazumilov/TAC_GAN
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
https://github.com/FireFYF/SlimCAE
https://github.com/FireFYF/modulatedautoencoder
https://github.com/IMvision12/keras-vision-models
Training data-efficient image transformers & distillation through attention
https://github.com/Iamanorange/DSSLIC
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
https://github.com/Layout-Parser/layout-parser
A Large Dataset of Historical Japanese Documents with Complex Layouts
https://github.com/LiuLei95/PyTorch-Learned-Image-Compression-with-GMM-and-Attention
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
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
https://github.com/MindCode-4/code-1/tree/main/udop
Text Role Classification in Scientific Charts Using Multimodal Transformers
https://github.com/MindCode-4/code-3/tree/main/deit
DiT: Self-supervised Pre-training for Document Image Transformer
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlm
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/MindSpore-scientific/code-7/tree/main/LayoutLMv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/Nikolai10/Checkerboard-Context-Model
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.