In this report, we introduce NICE
project\footnote{https://nice.lgresearch.ai/} and share the results and
outcomes of NICE challenge 2023. This project is designed to challenge the
computer vision community to develop robust image captioning models that
advance the state-of-the-art both in terms of accuracy and fairness. Through
the challenge, the image captioning models were tested using a new evaluation
dataset that includes a large variety of visual concepts from many domains.
There was no specific training data provided for the challenge, and therefore
the challenge entries were required to adapt to new types of image descriptions
that had not been seen during training. This report includes information on the
newly proposed NICE dataset, evaluation methods, challenge results, and
technical details of top-ranking entries. We expect that the outcomes of the
challenge will contribute to the improvement of AI models on various
vision-language tasks.

NICE 项目是挑战计算机视觉社区发展准确性和公平性都达到最新水平的强大图像字幕模型，通过测试使用一个包含多个领域的大量可视化概念的新评估数据集，没有提供特定的训练数据，要求适应在训练过程中没有见过的新型图像描述。该报告包括 NICE 数据集、评价方法、挑战结果和前列参赛作品的技术细节，预期挑战的结果将促进 AI 模型在各种视觉语言任务上的改进。