  Much of the recent progress in Vision-to-Language (V2L) problems has been
achieved through a combination of Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs). This approach does not explicitly represent
high-level semantic concepts, but rather seeks to progress directly from image
features to text. We propose here a method of incorporating high-level concepts
into the very successful CNN-RNN approach, and show that it achieves a
significant improvement on the state-of-the-art performance in both image
captioning and visual question answering. We also show that the same mechanism
can be used to introduce external semantic information and that doing so
further improves performance. In doing so we provide an analysis of the value
of high level semantic information in V2L problems.

Computer Vision and Pattern Recognition