TSC-Net: Theme-Style-Color Guided Artistic Image Aesthetics Assessment Network
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50069-5_17
Reference27 articles.
1. Yi, R., Tian, H., Gu, Z., Lai, Y.-K., Rosin, P.: Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method. ArXiv. (2023)
2. Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z.: RAPID: rating pictorial aesthetics using deep learning. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 457–466 (2014). https://doi.org/10.1145/2647868.2654927
3. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017). https://doi.org/10.1145/3065386
4. Talebi, H., Milanfar, P.: NIMA: neural image assessment. IEEE Trans. on Image Process. 27, 3998–4011 (2018). https://doi.org/10.1109/TIP.2018.2831899
5. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. ArXiv. (2017)
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