在图像美学评估领域，实验室已构建了入门学习资料库和路径，能帮助新同学快速上手；另外，我们提供数据集采集，标注和发布等流程的系列规范，立项了国际标准，并发布了全球首个以主题为核心的开源美学数据集；同时，我们还发布了第一个在通用、个性化和主题美学数据集上全SOTA的代码库；最后，我们还提供了图像美学评估的落地应用案例，能有效的支撑个人或厂商对于图像评估的需求。
相关论文和开源地址：
[1] Limin Liu, Shuai He, Anlong Ming*, Rui Xie, Huadong Ma, ELTA: An Enhancer against Long-Tail for Aesthetics-oriented Models, in Proceedings of the 41th international Conference on Machine Learning (ICML), 2024.
开源项目地址：https://github.com/mRobotit/Long-Tail-image-aesthetics-and-quality-assessment/tree/main
Shai He, Anlong Ming*, Shuntian Zheng, Haobin Zhong, Huadong Ma, EAT: An Enhancer for Aesthetics-Oriented Transformers, in Proceedings of the 31th ACM International Conference on Multimedia (MM), 2023.
开源项目地址： https://github.com/mRobotit/Image-Aesthetics-Assessment/tree/main
[3] Shai He, Anlong Ming*, Yaqi Li, Jinyuan Sun, ShunTian Zheng, Huadong Ma, Thinking Image Color Aesthetics Assessment: Models, Datasets and Benchmarks, in Proceedings of International Conference on Computer Vision (ICCV), 2023.
开源项目地址： https://github.com/mRobotit/Image-Color-Aesthetics-Assessment
[4] Shuai He, Yongchang Zhang, Dongxiang Jiang, Rui Xie, Anlong Ming*, Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks，the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI), 2022.
开源项目地址：https://github.com/mRobotit/TANet
