  Existing multi-style image captioning methods show promising results in
generating a caption with accurate visual content and desired linguistic style.
However, existing methods overlook the relationship between linguistic style
and visual content. To overcome this drawback, we propose style-aware
contrastive learning for multi-style image captioning. First, we present a
style-aware visual encoder with contrastive learning to mine potential visual
content relevant to style. Moreover, we propose a style-aware triplet contrast
objective to distinguish whether the image, style and caption matched. To
provide positive and negative samples for contrastive learning, we present
three retrieval schemes: object-based retrieval, RoI-based retrieval and
triplet-based retrieval, and design a dynamic trade-off function to calculate
retrieval scores. Experimental results demonstrate that our approach achieves
state-of-the-art performance. In addition, we conduct an extensive analysis to
verify the effectiveness of our method.
