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NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

2023-09-05 · 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

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.

📄 PDF Abstract BibTeX arXiv:2309.01961

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FairnessImage Captioning

Methods 이 논문이 사용한 방법론

Normalizing Flows Normalizing Flows are a method for constructing complex distributions by transforming a probability density through a series of invertible mappings. By repeatedly applying…
Affine Coupling 설명 없음
NICE NICE, or Non-Linear Independent Components Estimation is a framework for modeling complex high-dimensional densities. It is based on the idea that a good representation is…

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