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Recent advances in language and vision push forward the research ofcaptioning 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 adescription W_{1,2} comparing them, existing methods directly model { I_1, I_2} -> W_{1,2} mapping without the semantic understanding of individuals. In thispaper, we introduce a Learning-to-Compare (L2C) model, which learns tounderstand the semantic structures of these two images and compare them whilelearning to describe each one. We demonstrate that L2C benefits from acomparison between explicit semantic representations and single-image captions,and generalizes better on the new testing image pairs. It outperforms thebaseline on both automatic evaluation and human evaluation for theBirds-to-Words dataset.

L2C: Describing Visual Differences Needs Semantic Understanding of Individuals