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Image captioning is one of the most challenging tasks in AI, which aims toautomatically generate textual sentences for an image. Recent methods for imagecaptioning follow encoder-decoder framework that transforms the sequence ofsalient regions in an image into natural language descriptions. However, thesemodels usually lack the comprehensive understanding of the contextualinteractions reflected on various visual relationships between objects. In thispaper, we explore explicit and implicit visual relationships to enrichregion-level representations for image captioning. Explicitly, we buildsemantic graph over object pairs and exploit gated graph convolutional networks(Gated GCN) to selectively aggregate local neighbors' information. Implicitly,we draw global interactions among the detected objects through region-basedbidirectional encoder representations from transformers (Region BERT) withoutextra relational annotations. To evaluate the effectiveness and superiority ofour proposed method, we conduct extensive experiments on Microsoft COCObenchmark and achieve remarkable improvements compared with strong baselines.

Exploring Explicit and Implicit Visual Relationships for Image Captioning