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

• URL: http://arxiv.org/abs/2305.04183v1
• Date: Sun, 7 May 2023 03:59:31 GMT
• Title: OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese
• Authors: Nghia Hieu Nguyen, Duong T.D. Vo, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
• Abstract summary: We introduce the OpenViVQA dataset, the first large-scale dataset for visual question answering in Vietnamese. The dataset consists of 11,000+ images associated with 37,000+ question-answer pairs (QAs) Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C.
• Score: 2.7528170226206443
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question-answer pairs (QAs). Moreover, we proposed FST, QuMLAG, and MLPAG which fuse information from images and answers, then use these fused features to construct answers as humans iteratively. Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C. The dataset is available to encourage the research community to develop more generalized algorithms including transformers for low-resource languages such as Vietnamese.

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