The task of Visual Question Answering (VQA) involves generating a natural language answer to a question about an image
This requires a deep understanding of both the image's content and the question's context, which includes recognizing objects and scenes in the image and comprehending the semantics of the natural language question.
The objective of this task is to develop a system that can accurately generate a natural language answer based on the information present in the image and the question.
This requires solving several sub-problems in both computer vision (CV) and natural language processing (NLP), such as object detection, scene classification, and counting, making VQA considered a complex and comprehensive AI task.
The outcome of VQA task has far-reaching applications in a variety of fields. Some of the major outcomes of the VQA task include:
- Improved Image Understanding: VQA models help computers better understand the content of images, enabling them to identify objects, scenes, actions, and relationships.
- Enhanced Natural Language Processing: VQA models enable computers to process natural language questions and generate appropriate answers, thereby improving their ability to understand and respond to human language.
- Improved Human-Computer Interaction: VQA models facilitate human-like communication between computers and humans, enabling users to access information more easily and intuitively.