  A wide range of image captioning models has been developed, achieving
significant improvement based on popular metrics, such as BLEU, CIDEr, and
SPICE. However, although the generated captions can accurately describe the
image, they are generic for similar images and lack distinctiveness, i.e.,
cannot properly describe the uniqueness of each image. In this paper, we aim to
improve the distinctiveness of image captions through training with sets of
similar images. First, we propose a distinctiveness metric -- between-set CIDEr
(CIDErBtw) to evaluate the distinctiveness of a caption with respect to those
of similar images. Our metric shows that the human annotations of each image
are not equivalent based on distinctiveness. Thus we propose several new
training strategies to encourage the distinctiveness of the generated caption
for each image, which are based on using CIDErBtw in a weighted loss function
or as a reinforcement learning reward. Finally, extensive experiments are
conducted, showing that our proposed approach significantly improves both
distinctiveness (as measured by CIDErBtw and retrieval metrics) and accuracy
(e.g., as measured by CIDEr) for a wide variety of image captioning baselines.
These results are further confirmed through a user study.
