让AI理解越南文化并解释答案，提升视觉问答的透明度与文化敏感性。
VietMEAgent: Culturally-Aware Few-Shot Multimodal Explanation for Vietnamese Visual Question Answering
- 用文化物体检测+程序生成，实现答案与解释联动。
- 构建越南文化知识库，支持结构化推理与可视化解释。
- 适合研究文化感知AI或本地化多模态系统的学者使用。
当前视觉问答系统在面对特定文化内容时仍受限，主要因训练数据中文化知识不足且推理过程难以向用户解释。本文提出VietMEAgent，一种面向越南文化的多模态可解释框架。该方法融合文化物体检测主干网络与结构化程序生成层，形成答案预测与解释紧密耦合的流程。通过构建专门的越南文化实体知识库，提供显式背景信息；双模态解释模块结合注意力机制的视觉证据与人类可读的文本推理链。我们从公开资源构建了越南文化视觉问答数据集，验证了基于程序的方法在文化AI中的实用性。系统提供透明解释，揭示计算逻辑与文化背景，强调可解释性与文化敏感性，助力教育与文化传承。
原文摘要 · Abstract (English)
Contemporary Visual Question Answering (VQA) systems remain constrained when confronted with culturally specific content, largely because cultural knowledge is under-represented in training corpora and the reasoning process is not rendered interpretable to end users. This paper introduces VietMEAgent, a multimodal explainable framework engineered for Vietnamese cultural understanding. The method integrates a cultural object detection backbone with a structured program generation layer, yielding a pipeline in which answer prediction and explanation are tightly coupled. A curated knowledge base of Vietnamese cultural entities serves as an explicit source of background information, while a dual-modality explanation module combines attention-based visual evidence with structured, human-readable textual rationales. We further construct a Vietnamese Cultural VQA dataset sourced from public repositories and use it to demonstrate the practicality of programming-based methodologies for cultural AI. The resulting system provides transparent explanations that disclose both the computational rationale and the underlying cultural context, supporting education and cultural preservation with an emphasis on interpretability and cultural sensitivity.
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