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Urdu-CLEVR: A Novel Benchmark for Visual Reasoning in an Under-Resourced Linguistic Context

Visual Question Answering (VQA) bridges the gap between computer vision and natural language processing, yet progress remains largely confined to high-resource languages. For low-resource languages like Urdu, research is severely hindered by the total absence of large-scale reasoning-based datasets. To address this critical gap, we introduce the first synthetic Urdu VQA dataset modeled after the CLEVR framework, specifically designed to evaluate complex, multi-step visual reasoning. We conduct a rigorous comparative analysis using both transformer-based architectures (VisualBERT, LXMERT, ViLT) and neuro-symbolic models. Our results demonstrate that the neuro-symbolic approach achieves a superior accuracy of 85.3%, outperforming the strongest transformer baseline by 7.1% while maintaining competitive processing efficiency. This work establishes a primary benchmark for Urdu VQA, demonstrating that hybrid reasoning architectures provide a robust and scalable solution for advancing multimodal AI in under-resourced linguistic contexts.