Improving VQA and its Explanations \\ by Comparing Competing Explanations

Understand

Most recent state-of-the-art Visual Question Answering (VQA) systems are opaque black boxes that are only trained to fit the answer distribution given the question and visual content.

• As a result, these systems frequently take shortcuts, focusing on simple visual concepts or question priors.
• This phenomenon becomes more problematic as the questions become complex that requires more reasoning and commonsense knowledge.
• To address this issue, we present a novel framework that uses explanations for competing answers to help VQA systems select the correct answer.