arXiv 2603.096892026-03-11cs.CVcs.AI

AutoViVQA：一个大规模自动构建的数据集用于越南语视觉问答

AutoViVQA: A Large-Scale Automatically Constructed Dataset for Vietnamese Visual Question Answering

Faculty of Information Technology, University of Science, VNU-HCM（越南国家大学胡志明市分校信息科技学院）；Vietnam National University, Ho Chi Minh City（越南国家大学胡志明市分校）

Nguyen Anh Tuong, Phan Ba Duc, Nguyen Trung Quoc, Tran Dac Thinh, Dang Duy Lan, Nguyen Quoc Thinh, Tung Le

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AI总结：

本文提出AutoViVQA数据集，利用变压器架构进行越南语视觉问答，结合文本和视觉预训练，并在多语言环境下系统比较自动评估指标。

摘要：

视觉问答（VQA）是一个基本的多模态任务，要求模型联合理解视觉和文本信息。早期的VQA系统严重依赖语言偏见，推动了后续工作强调视觉定位和平衡数据集。随着大规模预训练变压器在文本和视觉领域取得成功——如PhoBERT用于越南语理解和Vision Transformers（ViT）用于图像表示学习——多模态融合取得了显著进展。对于越南语VQA，已经引入了几种数据集以促进低资源多模态学习的研究，包括ViVQA、OpenViVQA以及最近提出的ViTextVQA。这些资源使能够对整合语言和视觉特征的模型进行基准测试。VQA系统的评估通常采用最初为图像描述或机器翻译设计的自动指标，如BLEU、METEOR、CIDEr、Recall、Precision和F1-score。然而，最近的研究表明，大型语言模型可以进一步提高VQA任务中自动评估与人类判断之间的对齐。在本工作中，我们探索了基于变压器的越南语视觉问答，利用文本和视觉预训练，并在多语言环境下系统地比较自动评估指标。

英文摘要：

Visual Question Answering (VQA) is a fundamental multimodal task that requires models to jointly understand visual and textual information. Early VQA systems relied heavily on language biases, motivating subsequent work to emphasize visual grounding and balanced datasets. With the success of large-scale pre-trained transformers for both text and vision domains -- such as PhoBERT for Vietnamese language understanding and Vision Transformers (ViT) for image representation learning -- multimodal fusion has achieved remarkable progress. For Vietnamese VQA, several datasets have been introduced to promote research in low-resource multimodal learning, including ViVQA, OpenViVQA, and the recently proposed ViTextVQA. These resources enable benchmarking of models that integrate linguistic and visual features in the Vietnamese context. Evaluation of VQA systems often employs automatic metrics originally designed for image captioning or machine translation, such as BLEU, METEOR, CIDEr, Recall, Precision, and F1-score. However, recent research suggests that large language models can further improve the alignment between automatic evaluation and human judgment in VQA tasks. In this work, we explore Vietnamese Visual Question Answering using transformer-based architectures, leveraging both textual and visual pre-training while systematically comparing automatic evaluation metrics under multilingual settings.

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