FFGS: Feature Fusion with Gating Structure for Image Caption Generation

Automatically generating a natural language to describe the content of the given image is a challenging task in the interdisciplinary between computer vision and natural language processing. The task is challenging because computers not only need to recognize objects, their attributions and relationships between them in an image, but also these elements should be represented into a natural language sentence. This paper proposed a feature fusion with gating structure for image caption generation. First, the pre-trained VGG-19 is used as the image feature extractor. We use the FC-7 and CONV5-4 layer's outputs as the global and local image feature, respectively. Second, the image features and the corresponding sentence are imported into LSTM to learn their relationship. The global image feature is gated at each time-step before imported into LSTM while the local image feature used the attention model. Experimental results show our method outperform the state-of-the-art methods.