About This Architecture

Visual Question Answering (VQA) pipeline combining CNN/ViT visual encoders and BERT/GloVe text encoders to process image-question pairs through feature extraction, graph modeling, and GNN reasoning. Data flows through scene graph and semantic graph construction, followed by multi-hop graph neural network inference with external memory attention and bidirectional cross-modal fusion. The architecture validates inputs at each stage, applies fallback mechanisms for feature extraction and question encoding failures, and uses confidence thresholding before returning predicted answers. Fork this diagram to customize encoder architectures, adjust graph construction strategies, or integrate alternative attention mechanisms for your VQA application.

People also ask

How do Visual Question Answering systems combine image and text understanding to generate accurate answers?

VQA pipelines extract visual features using CNN/ViT encoders and text features using BERT/GloVe encoders, then construct scene and semantic graphs to model object relationships and concepts. Graph Neural Networks perform multi-hop reasoning over these graphs with external memory attention, while bidirectional cross-modal fusion aligns visual and linguistic representations before a softmax classifi