Visual Question Answering Model

Visual Question Answering (VQA) models aim to enable computers to understand and respond to questions about images, bridging the gap between visual perception and natural language understanding. Current research emphasizes improving model accuracy and robustness, particularly by addressing biases in training data and refining attention mechanisms to better align with human visual processing. This field is crucial for advancing artificial intelligence, with applications ranging from autonomous driving and assistive technologies for visually impaired individuals to improving the safety and fairness of image generation models. Furthermore, VQA models are increasingly used as evaluation tools for other AI systems, such as text-to-image generators.