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Found 6 papers, 2 papers with code
PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure
In this work, we propose a novel tree-based explanation technique, PEACH (Pretrained-embedding Explanation Across Contextual and Hierarchical Structure), that can explain how text-based documents are classified by using any pretrained contextual embeddings in a tree-based human-interpretable manner.
PDFVQA: A New Dataset for Real-World VQA on PDF Documents
Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions.
Form-NLU: Dataset for the Form Natural Language Understanding
Compared to general document analysis tasks, form document structure understanding and retrieval are challenging.
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals
We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily.
V-Doc : Visual questions answers with Documents
We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks.
V-Doc: Visual Questions Answers With Documents
We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks.
