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Found 11 papers, 5 papers with code
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding
Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities.
Diffusion-based Document Layout Generation
Our empirical analysis shows that our diffusion-based approach is comparable to or outperforming other previous methods for layout generation across various document datasets.
Understanding Long Documents with Different Position-Aware Attentions
Despite several successes in document understanding, the practical task for long document understanding is largely under-explored due to several challenges in computation and how to efficiently absorb long multimodal input.
Improving Structured Text Recognition with Regular Expression Biasing
We study the problem of recognizing structured text, i. e. text that follows certain formats, and propose to improve the recognition accuracy of structured text by specifying regular expressions (regexes) for biasing.
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
Text recognition is a long-standing research problem for document digitalization.
Ranked #3 on
Handwritten Text Recognition
on IAM
LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
In this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding.
Ranked #13 on
Document Image Classification
on RVL-CDIP
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.
Ranked #1 on
Key Information Extraction
on SROIE
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption
Due to this aligned representation learning, even pre-trained on the same downstream task dataset, TAP already boosts the absolute accuracy on the TextVQA dataset by +5. 4%, compared with a non-TAP baseline.
Multi-Scale 2D Temporal Adjacent Networks for Moment Localization with Natural Language
It is a challenging problem because a target moment may take place in the context of other temporal moments in the untrimmed video.
Shape retrieval of non-rigid 3d human models
In addition, further participants have also taken part, and we provide extra analysis of the retrieval results.
