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In this paper, we present our solution to the New frontiers for Zero-shotImage Captioning Challenge. Different from the traditional image captioningdatasets, this challenge includes a larger new variety of visual concepts frommany domains (such as COVID-19) as well as various image types (photographs,illustrations, graphics). For the data level, we collect external training datafrom Laion-5B, a large-scale CLIP-filtered image-text dataset. For the modellevel, we use OFA, a large-scale visual-language pre-training model based onhandcrafted templates, to perform the image captioning task. In addition, weintroduce contrastive learning to align image-text pairs to learn new visualconcepts in the pre-training stage. Then, we propose a similarity-bucketstrategy and incorporate this strategy into the template to force the model togenerate higher quality and more matching captions. Finally, byretrieval-augmented strategy, we construct a content-rich template, containingthe most relevant top-k captions from other image-text pairs, to guide themodel in generating semantic-rich captions. Our method ranks first on theleaderboard, achieving 105.17 and 325.72 Cider-Score in the validation and testphase, respectively.

The Solution for the CVPR2023 NICE Image Captioning Challenge