arXiv 2309.019612023-09-11cs.CV

NICE：CVPR 2023 零样本图像描述挑战赛

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

Taehoon Kim, Pyunghwan Ahn, Sangyun Kim, Sihaeng Lee, Mark Marsden, Alessandra Sala, Seung Hwan Kim, Bohyung Han, Kyoung Mu Lee, Honglak Lee, Kyounghoon Bae, Xi… 展开作者

Taehoon Kim, Pyunghwan Ahn, Sangyun Kim, Sihaeng Lee, Mark Marsden, Alessandra Sala, Seung Hwan Kim, Bohyung Han, Kyoung Mu Lee, Honglak Lee, Kyounghoon Bae, Xiangyu Wu, Yi Gao, Hailiang Zhang, Yang Yang, Weili Guo, Jianfeng Lu, Youngtaek Oh, Jae Won Cho, Dong-jin Kim, In So Kweon, Junmo Kim, Wooyoung Kang, Won Young Jhoo, Byungseok Roh, Jonghwan Mun, Solgil Oh, Kenan Emir Ak, Gwang-Gook Lee, Yan Xu, Mingwei Shen, Kyomin Hwang, Wonsik Shin, Kamin Lee, Wonhark Park, Dongkwan Lee, Nojun Kwak, Yujin Wang, Yimu Wang, Tiancheng Gu, Xingchang Lv, Mingmao Sun

备注：: Tech report, project page https://nice.lgresearch.ai/

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AI总结：

本文介绍了NICE项目及2023年零样本图像描述挑战赛，通过新评估数据集测试模型在无特定训练数据下的准确性与公平性，并分享数据集、评估方法及优胜方案技术细节，以推动视觉-语言AI模型的发展。

摘要：

在本报告中，我们介绍了NICE（New frontiers for zero-shot Image Captioning Evaluation）项目，并分享了2023年挑战赛的结果与成果。该项目旨在挑战计算机视觉社区，开发在准确性和公平性两方面均推动最新技术发展的鲁棒图像描述模型。在挑战赛中，图像描述模型使用了一个新的评估数据集进行测试，该数据集包含来自多个领域的大量视觉概念。挑战赛未提供特定的训练数据，因此参赛作品需要适应在训练期间未见过的全新图像描述类型。本报告包含了新提出的NICE数据集的信息、评估方法、挑战赛结果以及排名靠前作品的技术细节。我们期望该挑战赛的成果将有助于改进AI模型在各种视觉-语言任务上的表现。

英文摘要：

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. 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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。