  The focus of this survey is on the analysis of two modalities of multimodal
deep learning: image and text. Unlike classic reviews of deep learning where
monomodal image classifiers such as VGG, ResNet and Inception module are
central topics, this paper will examine recent multimodal deep models and
structures, including auto-encoders, generative adversarial nets and their
variants. These models go beyond the simple image classifiers in which they can
do uni-directional (e.g. image captioning, image generation) and bi-directional
(e.g. cross-modal retrieval, visual question answering) multimodal tasks.
Besides, we analyze two aspects of the challenge in terms of better content
understanding in deep multimodal applications. We then introduce current ideas
and trends in deep multimodal feature learning, such as feature embedding
approaches and objective function design, which are crucial in overcoming the
aforementioned challenges. Finally, we include several promising directions for
future research.
