  Change captioning is to describe the semantic change between a pair of
similar images in natural language. It is more challenging than general image
captioning, because it requires capturing fine-grained change information while
being immune to irrelevant viewpoint changes, and solving syntax ambiguity in
change descriptions. In this paper, we propose a neighborhood contrastive
transformer to improve the model's perceiving ability for various changes under
different scenes and cognition ability for complex syntax structure.
Concretely, we first design a neighboring feature aggregating to integrate
neighboring context into each feature, which helps quickly locate the
inconspicuous changes under the guidance of conspicuous referents. Then, we
devise a common feature distilling to compare two images at neighborhood level
and extract common properties from each image, so as to learn effective
contrastive information between them. Finally, we introduce the explicit
dependencies between words to calibrate the transformer decoder, which helps
better understand complex syntax structure during training. Extensive
experimental results demonstrate that the proposed method achieves the
state-of-the-art performance on three public datasets with different change
scenarios. The code is available at https://github.com/tuyunbin/NCT.
