DoPTA: Improving Document Layout Analysis using Patch-Text Alignment | Paper Citation Record

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This paper cites Docformer: End-to-end transformer for document understanding.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Docformer: End-to-end transformer for document understanding

Reference 1

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This paper cites Visual and textual deep feature fusion for document image classification.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Visual and textual deep feature fusion for document image classification

Reference 2

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This paper cites Eaml: Ensemble self-attention-based mu- tual learning network for document image classification,.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Eaml: Ensemble self-attention-based mu- tual learning network for document image classification,

Reference 3

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DoPTA: Improving Document Layout Analysis using Patch-Text Alignment BEiT: BERT Pre-Training of Image Transformers

Reference 4

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This paper cites Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, and Jovana Mitrovi´c.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, and Jovana Mitrovi´c

Reference 5

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This paper cites Cascade r-cnn: High quality object detection and instance segmentation.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Cascade r-cnn: High quality object detection and instance segmentation

Reference 6

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This paper cites Emerg- ing properties in self-supervised vision transformers.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Emerg- ing properties in self-supervised vision transformers

Reference 7

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This paper cites Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts

Reference 8

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This paper cites A simple framework for contrastive learning of visual representations.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment A simple framework for contrastive learning of visual representations

Reference 9

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This paper cites Big self-supervised mod- els are strong semi-supervised learners.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Big self-supervised mod- els are strong semi-supervised learners

Reference 10

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DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Uniter: Universal image-text representation learning

Reference 11

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DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Vision grid transformer for document layout analysis

Reference 12

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment An image is worth 16x16 words: Transformers for image recognition at scale

Reference 13

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This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Bootstrap your own latent-a new approach to self-supervised learning

Reference 14

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This paper cites Evaluation of deep convolutional nets for document image classification and retrieval.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Evaluation of deep convolutional nets for document image classification and retrieval

Reference 15

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This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Momentum contrast for unsupervised visual rep- resentation learning

Reference 16

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This paper cites Masked autoencoders are scalable vision learners.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Masked autoencoders are scalable vision learners

Reference 17

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This paper cites Bros: A pre-trained lan- guage model focusing on text and layout for better key infor- mation extraction from documents.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Bros: A pre-trained lan- guage model focusing on text and layout for better key infor- mation extraction from documents

Reference 18

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This paper cites Layoutlmv3: Pre-training for document ai with unified text and image masking.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Layoutlmv3: Pre-training for document ai with unified text and image masking

Reference 19

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This paper cites Icdar2019 compe- tition on scanned receipt ocr and information extraction.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Icdar2019 compe- tition on scanned receipt ocr and information extraction

Reference 20

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DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Unresolved cited work

Reference 21

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This paper cites Funsd: A dataset for form understanding in noisy scanned documents, 2019.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Funsd: A dataset for form understanding in noisy scanned documents, 2019

Reference 22

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 23

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DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Ocr-free document understanding transformer

Reference 24

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This paper cites Dit: Self-supervised pre-training for docu- ment image transformer.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Dit: Self-supervised pre-training for docu- ment image transformer

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This paper cites Grounded language-image pre-training, 2022.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Grounded language-image pre-training, 2022

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This paper cites Grounded language-image pre-training.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Grounded language-image pre-training

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This paper cites DocBank: A Benchmark Dataset for Document Layout Analysis.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment DocBank: A Benchmark Dataset for Document Layout Analysis

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This paper cites Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding

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This paper cites Docvqa: A dataset for vqa on document images.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Docvqa: A dataset for vqa on document images

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DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Infographicvqa

Reference 31

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DoPTA: Improving Document Layout Analysis using Patch-Text Alignment DINOv2: Learning Robust Visual Features without Supervision

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This paper cites {CORD}: A consolidated receipt dataset for post-{ocr} parsing.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment {CORD}: A consolidated receipt dataset for post-{ocr} parsing

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This paper cites Doclaynet: A large human- annotated dataset for document-layout segmentation.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Doclaynet: A large human- annotated dataset for document-layout segmentation

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This paper cites Going full-tilt boogie on document understanding with text-image-layout transformer.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Going full-tilt boogie on document understanding with text-image-layout transformer

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This paper cites Learning transferable visual models from natural language supervi- sion.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Learning transferable visual models from natural language supervi- sion

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Observation 62979f90-35aa-4ffc-9a99-a2bb6acb48b9 · outbound

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DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Imagenet large scale visual recognition challenge

Reference 37

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This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Laion-5b: An open large-scale dataset for training next generation image-text models

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This paper cites Complex document information processing (cdip) dataset, 2022.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Complex document information processing (cdip) dataset, 2022

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Observation 345b4aa3-8ecc-4782-8dd1-45a40c9bd442 · outbound

This paper cites Kleister: key in- formation extraction datasets involving long documents with complex layouts.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Kleister: key in- formation extraction datasets involving long documents with complex layouts

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Observation 5cabec59-b2ba-4b11-8a65-3bbbc638f022 · outbound

This paper cites Vl-bert: Pre-training of generic visual- linguistic representations.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Vl-bert: Pre-training of generic visual- linguistic representations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:40:39.977499Z

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This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Revisiting unreasonable effectiveness of data in deep learning era

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Observation 8e70fc14-c584-4731-89ec-6bc277c3b6d3 · outbound

This paper cites Unifying vision, text, and layout for universal document processing.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Unifying vision, text, and layout for universal document processing

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Observation 7bb084c8-b4ae-4cd3-9a6d-86c1bb614771 · outbound

This paper cites Yfcc100m: The new data in multimedia research.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Yfcc100m: The new data in multimedia research

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Observation c4e96163-fed6-436d-98d9-d8d38625d646 · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention, 2021.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Training data-efficient image transformers & distillation through at- tention, 2021

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This paper cites Detectron2.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Detectron2

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Observation b5a48ba7-8102-4142-8dab-dd75a01539d3 · outbound

This paper cites Aggregated residual transformations for deep neural networks, 2017.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Aggregated residual transformations for deep neural networks, 2017

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Observation 90ce9152-23a7-469c-bfe6-609d473f6bb1 · outbound

This paper cites Layoutlm: Pre-training of text and layout for document image understanding.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Layoutlm: Pre-training of text and layout for document image understanding

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This paper cites LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

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This paper cites FILIP: Fine-grained interactive language- image pre-training.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment FILIP: Fine-grained interactive language- image pre-training

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:40:39.895575Z

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Observation 3c7e747a-4390-4ab8-b9ed-b9d9b70a1ea0 · outbound

This paper cites StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training

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This paper cites Sigmoid loss for language image pre-training,.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Sigmoid loss for language image pre-training,

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Observation 2c154ed6-f2a5-4984-8f95-5f5575dfe960 · outbound

This paper cites Zhang, H.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Zhang, H

Reference 53

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Observation 58d87be3-8112-40c9-9d50-d2d0c2364bd7 · outbound

This paper cites Pub- laynet: largest dataset ever for document layout analysis.

DoPTA: Improving Document Layout Analysis using Patch-Text Alignment Pub- laynet: largest dataset ever for document layout analysis

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:40:39.850776Z

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