  Existing image captioning methods just focus on understanding the
relationship between objects or instances in a single image, without exploring
the contextual correlation existed among contextual image. In this paper, we
propose Dual Graph Convolutional Networks (Dual-GCN) with transformer and
curriculum learning for image captioning. In particular, we not only use an
object-level GCN to capture the object to object spatial relation within a
single image, but also adopt an image-level GCN to capture the feature
information provided by similar images. With the well-designed Dual-GCN, we can
make the linguistic transformer better understand the relationship between
different objects in a single image and make full use of similar images as
auxiliary information to generate a reasonable caption description for a single
image. Meanwhile, with a cross-review strategy introduced to determine
difficulty levels, we adopt curriculum learning as the training strategy to
increase the robustness and generalization of our proposed model. We conduct
extensive experiments on the large-scale MS COCO dataset, and the experimental
results powerfully demonstrate that our proposed method outperforms recent
state-of-the-art approaches. It achieves a BLEU-1 score of 82.2 and a BLEU-2
score of 67.6. Our source code is available at {\em
\color{magenta}{\url{https://github.com/Unbear430/DGCN-for-image-captioning}}}.
