  In this paper, we introduce a novel approach to novel object captioning which
employs relative contrastive learning to learn visual and semantic alignment.
Our approach maximizes compatibility between regions and object tags in a
contrastive manner. To set up a proper contrastive learning objective, for each
image, we augment tags by leveraging the relative nature of positive and
negative pairs obtained from foundation models such as CLIP. We then use the
rank of each augmented tag in a list as a relative relevance label to contrast
each top-ranked tag with a set of lower-ranked tags. This learning objective
encourages the top-ranked tags to be more compatible with their image and text
context than lower-ranked tags, thus improving the discriminative ability of
the learned multi-modality representation. We evaluate our approach on two
datasets and show that our proposed RCA-NOC approach outperforms
state-of-the-art methods by a large margin, demonstrating its effectiveness in
improving vision-language representation for novel object captioning.
