Quality-Aware CLIP for Blind Image Quality Assessment
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8537-1_32
Reference25 articles.
1. Gao, P., et al.: CLIP-adapter: better vision-language models with feature adapters. arXiv preprint arXiv:2110.04544 (2021)
2. Ghadiyaram, D., Bovik, A.C.: Massive online crowdsourced study of subjective and objective picture quality. IEEE Trans. Image Process. 25(1), 372–387 (2015)
3. Golestaneh, S.A., Dadsetan, S., Kitani, K.M.: No-reference image quality assessment via transformers, relative ranking, and self-consistency. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1220–1230 (2022)
4. Hosu, V., Lin, H., Sziranyi, T., Saupe, D.: KonIQ-10k: an ecologically valid database for deep learning of blind image quality assessment. IEEE Trans. Image Process. 29, 4041–4056 (2020)
5. Ke, J., Wang, Q., Wang, Y., Milanfar, P., Yang, F.: MUSIQ: multi-scale image quality transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5148–5157 (2021)
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