L2C: Describing Visual Differences Needs Semantic Understanding of Individuals

Understand

Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs.

• Suppose there are two images, I_1 and I_2, and the task is to generate a description W_{1,2} comparing them, existing methods directly model { I_1, I_2 } -> W_{1,2} mapping without the semantic understanding of individuals.
• In this paper, we introduce a Learning-to-Compare (L2C) model, which learns to understand the semantic structures of these two images and compare them while learning to describe each one.
• We demonstrate that L2C benefits from a comparison between explicit semantic representations and single-image captions, and generalizes better on the new testing image pairs.