Analyzing the Behavior of Visual Question Answering Models

Understand

Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA).

• The performance of most models is clustered around 60-70%.
• In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress.
• We analyze two models, one each from two major classes of VQA models -- with-attention and without-attention and show the similarities and differences in the behavior of these models.