VQA Datasets

Visual Question Answering (VQA) datasets are collections of images paired with questions and answers, used to train and evaluate AI models capable of understanding and reasoning about visual information. Current research focuses on improving model performance through techniques like attention mechanisms guided by image segmentation, question decomposition for multi-hop reasoning, and leveraging external knowledge bases or large language models. These advancements are driving the development of more robust and accurate VQA systems, with applications ranging from assisting users with software-related questions to enhancing blind video quality assessment and improving multimodal information retrieval in complex documents. The creation of diverse and challenging datasets, including those focused on multilingual capabilities and safety considerations, is crucial for pushing the boundaries of this rapidly evolving field.