  Multi-change captioning aims to describe complex and coupled changes within
an image pair in natural language. Compared with single-change captioning, this
task requires the model to have higher-level cognition ability to reason an
arbitrary number of changes. In this paper, we propose a novel context-aware
difference distilling (CARD) network to capture all genuine changes for
yielding sentences. Given an image pair, CARD first decouples context features
that aggregate all similar/dissimilar semantics, termed common/difference
context features. Then, the consistency and independence constraints are
designed to guarantee the alignment/discrepancy of common/difference context
features. Further, the common context features guide the model to mine locally
unchanged features, which are subtracted from the pair to distill locally
difference features. Next, the difference context features augment the locally
difference features to ensure that all changes are distilled. In this way, we
obtain an omni-representation of all changes, which is translated into
linguistic sentences by a transformer decoder. Extensive experiments on three
public datasets show CARD performs favourably against state-of-the-art
methods.The code is available at https://github.com/tuyunbin/CARD.
