  Supervised visual captioning models typically require a large scale of images
or videos paired with descriptions in a specific language (i.e., the
vision-caption pairs) for training. However, collecting and labeling
large-scale datasets is time-consuming and expensive for many scenarios and
languages. Therefore, sufficient labeled pairs are usually not available. To
deal with the label shortage problem, we present a simple yet effective
zero-shot approach MultiCapCLIP that can generate visual captions for different
scenarios and languages without any labeled vision-caption pairs of downstream
datasets. In the training stage, MultiCapCLIP only requires text data for
input. Then it conducts two main steps: 1) retrieving concept prompts that
preserve the corresponding domain knowledge of new scenarios; 2) auto-encoding
the prompts to learn writing styles to output captions in a desired language.
In the testing stage, MultiCapCLIP instead takes visual data as input directly
to retrieve the concept prompts to generate the final visual descriptions. The
extensive experiments on image and video captioning across four benchmarks and
four languages (i.e., English, Chinese, German, and French) confirm the
effectiveness of our approach. Compared with state-of-the-art zero-shot and
weakly-supervised methods, our method achieves 4.8% and 21.5% absolute
improvements in terms of BLEU@4 and CIDEr metrics. Our code is available at
https://github.com/yangbang18/MultiCapCLIP.
