OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese

Understand

In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge.

• The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers.
• Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English.
• However, available datasets narrow the VQA task as the answers selection task or answer classification task.