  Describing images using natural language is widely known as image captioning,
which has made consistent progress due to the development of computer vision
and natural language generation techniques. Though conventional captioning
models achieve high accuracy based on popular metrics, i.e., BLEU, CIDEr, and
SPICE, the ability of captions to distinguish the target image from other
similar images is under-explored. To generate distinctive captions, a few
pioneers employ contrastive learning or re-weighted the ground-truth captions,
which focuses on one single input image. However, the relationships between
objects in a similar image group (e.g., items or properties within the same
album or fine-grained events) are neglected. In this paper, we improve the
distinctiveness of image captions using a Group-based Distinctive Captioning
Model (GdisCap), which compares each image with other images in one similar
group and highlights the uniqueness of each image. In particular, we propose a
group-based memory attention (GMA) module, which stores object features that
are unique among the image group (i.e., with low similarity to objects in other
images). These unique object features are highlighted when generating captions,
resulting in more distinctive captions. Furthermore, the distinctive words in
the ground-truth captions are selected to supervise the language decoder and
GMA. Finally, we propose a new evaluation metric, distinctive word rate
(DisWordRate) to measure the distinctiveness of captions. Quantitative results
indicate that the proposed method significantly improves the distinctiveness of
several baseline models, and achieves the state-of-the-art performance on both
accuracy and distinctiveness. Results of a user study agree with the
quantitative evaluation and demonstrate the rationality of the new metric
DisWordRate.
