Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering

Understand

Many vision and language tasks require commonsense reasoning beyond data-driven image and natural language processing.

• Here we adopt Visual Question Answering (VQA) as an example task, where a system is expected to answer a question in natural language about an image.
• Current state-of-the-art systems attempted to solve the task using deep neural architectures and achieved promising performance.
• However, the resulting systems are generally opaque and they struggle in understanding questions for which extra knowledge is required.