  Descriptive region features extracted by object detection networks have
played an important role in the recent advancements of image captioning.
However, they are still criticized for the lack of contextual information and
fine-grained details, which in contrast are the merits of traditional grid
features. In this paper, we introduce a novel Dual-Level Collaborative
Transformer (DLCT) network to realize the complementary advantages of the two
features. Concretely, in DLCT, these two features are first processed by a
novelDual-way Self Attenion (DWSA) to mine their intrinsic properties, where a
Comprehensive Relation Attention component is also introduced to embed the
geometric information. In addition, we propose a Locality-Constrained Cross
Attention module to address the semantic noises caused by the direct fusion of
these two features, where a geometric alignment graph is constructed to
accurately align and reinforce region and grid features. To validate our model,
we conduct extensive experiments on the highly competitive MS-COCO dataset, and
achieve new state-of-the-art performance on both local and online test sets,
i.e., 133.8% CIDEr-D on Karpathy split and 135.4% CIDEr on the official split.
Code is available at https://github.com/luo3300612/image-captioning-DLCT.
