English Visual Question Answering: Building a Culturally Relevant Dataset from Image Captions

Visual Question Answering (VQA) is a challenging multimodal task requiring joint understanding of visual content and natural language questions. Traditional VQA systems rely on complex attention-based architectures demanding significant computational resources and GPU training. This paper proposes an efficient and scalable VQA system using a pretrained CLIP (Contrastive Language–Image Pretraining) ViT-B/32 model for open-ended English language queries. The proposed approach extracts semantically aligned image and question embeddings using a frozen CLIP backbone and combines them through a lightweight Multi-Layer Perceptron (MLP) classifier for answer prediction. Experiments on the VizWiz dataset — a real-world benchmark of images captured by visually impaired users — demonstrate competitive performance, achieving a Top-1 accuracy of 40.6% and Top-5 accuracy of 71.7%, trained entirely on CPU without end-to-end fine-tuning. A Flask-based web application supporting user authentication, image upload, and real-time Top-5 predictions with confidence scores is also demonstrated.