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Recently, the state-of-the-art models for image captioning have overtakenhuman performance based on the most popular metrics, such as BLEU, METEOR,ROUGE, and CIDEr. Does this mean we have solved the task of image captioning?The above metrics only measure the similarity of the generated caption to thehuman annotations, which reflects its accuracy. However, an image contains manyconcepts and multiple levels of detail, and thus there is a variety of captionsthat express different concepts and details that might be interesting fordifferent humans. Therefore only evaluating accuracy is not sufficient formeasuring the performance of captioning models --- the diversity of thegenerated captions should also be considered. In this paper, we proposed a newmetric for measuring the diversity of image captions, which is derived fromlatent semantic analysis and kernelized to use CIDEr similarity. We conductextensive experiments to re-evaluate recent captioning models in the context ofboth diversity and accuracy. We find that there is still a large gap betweenthe model and human performance in terms of both accuracy and diversity and themodels that have optimized accuracy (CIDEr) have low diversity. We also showthat balancing the cross-entropy loss and CIDEr reward in reinforcementlearning during training can effectively control the tradeoff between diversityand accuracy of the generated captions.

Describing like humans: on diversity in image captioning