  We describe a very simple bag-of-words baseline for visual question
answering. This baseline concatenates the word features from the question and
CNN features from the image to predict the answer. When evaluated on the
challenging VQA dataset [2], it shows comparable performance to many recent
approaches using recurrent neural networks. To explore the strength and
weakness of the trained model, we also provide an interactive web demo and
open-source code. .
