VietMEAgent: Culturally-Aware Few-Shot Multimodal Explanation for Vietnamese Visual Question Answering
This addresses the issue of cultural under-representation in VQA for Vietnamese users, with potential applications in education and cultural preservation, though it is incremental as it builds on existing programming-based methodologies.
The paper tackles the problem of Visual Question Answering (VQA) systems struggling with culturally specific content by introducing VietMEAgent, a framework for Vietnamese cultural understanding that integrates cultural object detection and structured program generation, resulting in a system that provides transparent explanations combining visual evidence and textual rationales.
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.