Style-Aware Contrastive Learning for Multi-Style Image Captioning
Yucheng Zhou, Guodong Long · 2023
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, RoIbased 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-theart performance.In addition, we conduct an extensive analysis to verify the effectiveness of our method.