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[bibtex]@InProceedings{Kim_2024_CVPR, author = {Kim, Taehoon and Ahn, Pyunghwan and Kim, Sangyun and Lee, Sihaeng and Marsden, Mark and Sala, Alessandra and Kim, Seung Hwan and Han, Bohyung and Lee, Kyoung Mu and Lee, Honglak and Bae, Kyounghoon and Wu, Xiangyu and Gao, Yi and Zhang, Hailiang and Yang, Yang and Guo, Weili and Lu, Jianfeng and Oh, Youngtaek and Cho, Jae Won and Kim, Dong-Jin and Kweon, In So and Kim, Junmo and Kang, Wooyoung and Jhoo, Won Young and Roh, Byungseok and Mun, Jonghwan and Oh, Solgil and Ak, Kenan Emir and Lee, Gwang-Gook and Xu, Yan and Shen, Mingwei and Hwang, Kyomin and Shin, Wonsik and Lee, Kamin and Park, Wonhark and Lee, Dongkwan and Kwak, Nojun and Wang, Yujin and Wang, Yimu and Gu, Tiancheng and Lv, Xingchang and Sun, Mingmao}, title = {NICE: CVPR 2023 Challenge on Zero-shot Image Captioning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {7356-7365} }
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
Abstract
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.
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