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The Convolutional Neural Network (CNN) has been the dominant image featureextractor in computer vision for years. However, it fails to get therelationship between images/objects and their hierarchical interactions whichcan be helpful for representing and describing an image. In this paper, wepropose a new design for image caption under a general encoder-decoderframework. It takes into account the hierarchical interactions betweendifferent abstraction levels of visual information in the images and theirbounding-boxes. Specifically, we present CNN plus Graph Convolutional Network(GCN) architecture that novelly integrates both semantic and spatial visualrelationships into image encoder. The representations of regions in an imageand the connections between images are refined by leveraging graph structurethrough GCN. With the learned multi-level features, our model capitalizes onthe Transformer-based decoder for description generation. We conductexperiments on the COCO image captioning dataset. Evaluations show that ourproposed model outperforms the previous state-of-the-art models in the task ofimage caption, leading to a better performance in terms of all evaluationmetrics.

Better Understanding Hierarchical Visual Relationship for Image Caption