Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning

Understand

We study how to generate captions that are not only accurate in describing an image but also discriminative across different images.

• The problem is both fundamental and interesting, as most machine-generated captions, despite phenomenal research progresses in the past several years, are expressed in a very monotonic and featureless format.
• While such captions are normally accurate, they often lack important characteristics in human languages - distinctiveness for each caption and diversity for different images.
• To address this problem, we propose a novel conditional generative adversarial network for generating diverse captions across images.