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Found 8 papers, 4 papers with code
ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering by Understanding Vietnamese Text in Images
To this end, we introduce a novel dataset, ViOCRVQA (Vietnamese Optical Character Recognition - Visual Question Answering dataset), consisting of 28, 000+ images and 120, 000+ question-answer pairs.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in Images
Visual Question Answering (VQA) is a complicated task that requires the capability of simultaneously processing natural language and images.
Multimodal Deep Learning
Optical Character Recognition (OCR)
+5
PAT: Parallel Attention Transformer for Visual Question Answering in Vietnamese
Based on these two novel modules, we introduce the Parallel Attention Transformer (PAT), achieving the best accuracy compared to all baselines on the benchmark ViVQA dataset and other SOTA methods including SAAA and MCAN.
UIT-OpenViIC: A Novel Benchmark for Evaluating Image Captioning in Vietnamese
To contribute to the low-resources research community as in Vietnam, we introduce a novel image captioning dataset in Vietnamese, the Open-domain Vietnamese Image Captioning dataset (UIT-OpenViIC).
OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese
The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers.
EVJVQA Challenge: Multilingual Visual Question Answering
Visual Question Answering (VQA) is a challenging task of natural language processing (NLP) and computer vision (CV), attracting significant attention from researchers.
UIT-HWDB: Using Transferring Method to Construct A Novel Benchmark for Evaluating Unconstrained Handwriting Image Recognition in Vietnamese
Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages.
VieCap4H-VLSP 2021: ObjectAoA-Enhancing performance of Object Relation Transformer with Attention on Attention for Vietnamese image captioning
Image captioning is currently a challenging task that requires the ability to both understand visual information and use human language to describe this visual information in the image.
