  Distinctive Image Captioning (DIC) -- generating distinctive captions that
describe the unique details of a target image -- has received considerable
attention over the last few years. A recent DIC method proposes to generate
distinctive captions by comparing the target image with a set of
semantic-similar reference images, i.e., reference-based DIC (Ref-DIC). It aims
to force the generated captions to distinguish between the target image and the
reference image. To ensure Ref-DIC models really perceive the unique objects
(or attributes) in target images, we propose two new Ref-DIC benchmarks and
develop a Transformer-based Ref-DIC baseline TransDIC. The model only extracts
visual features from the target image, but also encodes the differences between
objects in the target and reference images. Taking one step further, we propose
a stronger TransDIC++, which consists of an extra contrastive learning module
to make full use of the reference images. This new module is model-agnostic,
which can be easily incorporated into various Ref-DIC architectures. Finally,
for more trustworthy benchmarking, we propose a new evaluation metric named
DisCIDEr for Ref-DIC, which evaluates both the accuracy and distinctiveness of
the generated captions. Experimental results demonstrate that our TransDIC++
can generate distinctive captions. Besides, it outperforms several
state-of-the-art models on the two new benchmarks over different metrics.
