Holistic Evaluation of Text-to-Image Models: Human evaluation procedure
2024-10-14 01:45:20 Author: hackernoon.com(查看原文) 阅读量:0 收藏

Authors:

(1) Tony Lee, Stanford with Equal contribution;

(2) Michihiro Yasunaga, Stanford with Equal contribution;

(3) Chenlin Meng, Stanford with Equal contribution;

(4) Yifan Mai, Stanford;

(5) Joon Sung Park, Stanford;

(6) Agrim Gupta, Stanford;

(7) Yunzhi Zhang, Stanford;

(8) Deepak Narayanan, Microsoft;

(9) Hannah Benita Teufel, Aleph Alpha;

(10) Marco Bellagente, Aleph Alpha;

(11) Minguk Kang, POSTECH;

(12) Taesung Park, Adobe;

(13) Jure Leskovec, Stanford;

(14) Jun-Yan Zhu, CMU;

(15) Li Fei-Fei, Stanford;

(16) Jiajun Wu, Stanford;

(17) Stefano Ermon, Stanford;

(18) Percy Liang, Stanford.

We used the Amazon Mechanical Turk (MTurk) platform to receive human feedback on the AIgenerated images. Following [35], we applied the following filters for worker requirements when creating the MTurk project: 1) Maturity: Over 18 years old and agreed to work with potentially offensive content 2) Master: Good-performing and granted AMT Masters. We required five different annotators per sample. Figure 6 shows the design layout of the survey.

Based on an hourly wage of $16 per hour, each annotator was paid $0.02 for answering a single multiple-choice question. The total amount spent for human annotations was $13,433.55.


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