The field of multimodal vision-language understanding is moving towards addressing the challenges of complex and diverse real-world scenarios. Researchers are focusing on developing models that can effectively integrate visual and textual features to improve performance in tasks such as visual question answering. A key direction is the development of benchmarks and datasets for low-resource languages, which will facilitate the creation of more inclusive AI systems. Noteworthy papers in this area include: MEENA, a dataset designed to evaluate Persian vision-language models, which introduces a bilingual structure to assess cross-linguistic performance. PlantVillageVQA, a large-scale visual question answering dataset for plant science, which provides a publicly available and expert-verified database to enhance diagnostic accuracy for plant disease identifications.