Image Aesthetics Assessment Based on Visual Perception and Textual Semantic Understanding
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3626-3_4
Reference59 articles.
1. Ji, Z., Chen, K., He, Y., et al.: Heterogeneous memory enhanced graph reasoning network for cross-modal retrieval. Sci. China Inf. Sci. 65(7), 172104 (2022)
2. Wang, L., Wang, X., Yamasaki, T.: Image aesthetics prediction using multiple patches preserving the original aspect ratio of contents. Multimed. Tools Appl. 82, 2783–2804 (2023)
3. Mei, S., Geng, Y., Hou, J., et al.: Learning hyperspectral images from RGB images via a coarse-to-fine CNN. Sci. China Inf. Sci. 65, 1–14 (2022)
4. Cheng, G., Lai, P., Gao, D., et al.: Class attention network for image recognition. Sci. China Inf. Sci. 66(3), 132105 (2023)
5. Pandit, A., Animesh, Gautam, B.K., Agarwal, R.: Image aesthetic score prediction using image captioning. In: Kumar, A., Mozar, S., Haase, J. (eds.) Advances in Cognitive Science and Communications, ICCCE 2023. Cognitive Science and Technology. Springer, Singapore (2023)
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