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 1 , and open-source code 2 .
Another model (iBOWImg) REF , is a bag-of-words baseline which concatenates the word features from the question and convolutional features from the image to predict the answer, which shows results competitive with many recent more complex approaches using recurrent neural networks (LSTMImg).
Simple Baseline for Visual Question Answering
ArXiv
ArXiv
Computer Science