Search Results for author:
Found 9 papers, 4 papers with code
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.
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering
To make explicit teaching of the relations between the two modalities, we proposed and integrated two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention.
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.
Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis
Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications.
Local Interpretations for Explainable Natural Language Processing: A Survey
As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models.
Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images
The main contributions of this paper are proposing the Financial Documents dataset with table-area annotations, the superior detection model and the rule-based layout segmentation technique for the tabular data extraction from PDF files.
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks
We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input.
VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks
We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input.
Ranked #24 on
Text-to-Image Generation
on COCO
(Inception score metric)
REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question Answering
Visual question answering (VQA) is a challenging multi-modal task that requires not only the semantic understanding of both images and questions, but also the sound perception of a step-by-step reasoning process that would lead to the correct answer.
