VQA Dataset

Visual Question Answering (VQA) datasets are crucial for training and evaluating AI models that can understand and reason about images and text. Current research emphasizes creating larger, higher-quality datasets with diverse question types and realistic scenarios, including those requiring external knowledge or addressing the challenges of unanswerable questions. This involves developing novel data generation techniques and exploring model architectures that effectively integrate visual and textual information, often leveraging large language and vision models. Improved VQA datasets and models have significant implications for various applications, including image retrieval, assistive technologies, and enhancing human-computer interaction.