  Significant progress has been made in recent years in image captioning, an
active topic in the fields of vision and language. However, existing methods
tend to yield overly general captions and consist of some of the most frequent
words/phrases, resulting in inaccurate and indistinguishable descriptions (see
Figure 1). This is primarily due to (i) the conservative characteristic of
traditional training objectives that drives the model to generate correct but
hardly discriminative captions for similar images and (ii) the uneven word
distribution of the ground-truth captions, which encourages generating highly
frequent words/phrases while suppressing the less frequent but more concrete
ones. In this work, we propose a novel global-local discriminative objective
that is formulated on top of a reference model to facilitate generating
fine-grained descriptive captions. Specifically, from a global perspective, we
design a novel global discriminative constraint that pulls the generated
sentence to better discern the corresponding image from all others in the
entire dataset. From the local perspective, a local discriminative constraint
is proposed to increase attention such that it emphasizes the less frequent but
more concrete words/phrases, thus facilitating the generation of captions that
better describe the visual details of the given images. We evaluate the
proposed method on the widely used MS-COCO dataset, where it outperforms the
baseline methods by a sizable margin and achieves competitive performance over
existing leading approaches. We also conduct self-retrieval experiments to
demonstrate the discriminability of the proposed method.
