  Change captioning tasks aim to detect changes in image pairs observed before
and after a scene change and generate a natural language description of the
changes. Existing change captioning studies have mainly focused on scenes with
a single change. However, detecting and describing multiple changed parts in
image pairs is essential for enhancing adaptability to complex scenarios. We
solve the above issues from three aspects: (i) We propose a CG-based
multi-change captioning dataset; (ii) We benchmark existing state-of-the-art
methods of single change captioning on multi-change captioning; (iii) We
further propose Multi-Change Captioning transformers (MCCFormers) that identify
change regions by densely correlating different regions in image pairs and
dynamically determines the related change regions with words in sentences. The
proposed method obtained the highest scores on four conventional change
captioning evaluation metrics for multi-change captioning. In addition,
existing methods generate a single attention map for multiple changes and lack
the ability to distinguish change regions. In contrast, our proposed method can
separate attention maps for each change and performs well with respect to
change localization. Moreover, the proposed framework outperformed the previous
state-of-the-art methods on an existing change captioning benchmark,
CLEVR-Change, by a large margin (+6.1 on BLEU-4 and +9.7 on CIDEr scores),
indicating its general ability in change captioning tasks.
