The Solution for the CVPR2023 NICE Image Captioning Challenge

• URL: http://arxiv.org/abs/2310.06879v2
• Date: Thu, 4 Jul 2024 03:56:16 GMT
• Title: The Solution for the CVPR2023 NICE Image Captioning Challenge
• Authors: Xiangyu Wu, Yi Gao, Hailiang Zhang, Yang Yang, Weili Guo, Jianfeng Lu,
• Abstract summary: We present our solution to the New frontiers for Zero-shot Image Captioning Challenge. This challenge includes a larger new variety of visual concepts from many domains. For the data level, we collect external training data from Laion-5B. For the model level, we use OFA, a large-scale visual-language pre-training model.
• Score: 11.37047794237074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present our solution to the New frontiers for Zero-shot Image Captioning Challenge. Different from the traditional image captioning datasets, this challenge includes a larger new variety of visual concepts from many domains (such as COVID-19) as well as various image types (photographs, illustrations, graphics). For the data level, we collect external training data from Laion-5B, a large-scale CLIP-filtered image-text dataset. For the model level, we use OFA, a large-scale visual-language pre-training model based on handcrafted templates, to perform the image captioning task. In addition, we introduce contrastive learning to align image-text pairs to learn new visual concepts in the pre-training stage. Then, we propose a similarity-bucket strategy and incorporate this strategy into the template to force the model to generate higher quality and more matching captions. Finally, by retrieval-augmented strategy, we construct a content-rich template, containing the most relevant top-k captions from other image-text pairs, to guide the model in generating semantic-rich captions. Our method ranks first on the leaderboard, achieving 105.17 and 325.72 Cider-Score in the validation and test phase, respectively.

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