Papers

论文集合

通用美学评估

2023

“BMI-Net: A Brain-Inspired Multimodal Interaction Network for Image Aesthetic Assessment.” MM2023, 2023.
Ke, Junjie, et al. VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining. arXiv:2303.14302, arXiv, 2 June 2023. arXiv.org, http://arxiv.org/abs/2303.14302 .
“Multimodal Color Recommendation in Vector Graphic Documents.” MM2023, 2017.
Vera Nieto, Daniel, et al. “A Retrieval System for Images and Videos Based on Aesthetic Assessment of Visuals.” Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2023, pp. 3180–84. DOI.org (Crossref), https://doi.org/10.1145/3539618.3591817 (opens in a new tab) .
Wu, Haoning, et al. Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives. arXiv:2211.04894, arXiv, 7 Mar. 2023. arXiv.org, http://arxiv.org/abs/2211.04894 .
Xu, Liwu, et al. CLIP Brings Better Features to Visual Aesthetics Learners. arXiv:2307.15640, arXiv, 28 July 2023. arXiv.org, http://arxiv.org/abs/2307.15640 . 这篇文章用的CLIP，可以重点关注下
Novel Groundtruth Transformations for the Aesthetic Assessment Problem | Elsevier Enhanced Reader. https://doi.org/10.1016/j.ipm.2023.103368 (opens in a new tab) . Accessed 24 Apr. 2023.
Wu, Xiaoshi, et al. "Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis." arXiv preprint arXiv:2306.09341 (2023).

2022

He, Shuai, et al. "Rethinking image aesthetics assessment: Models, datasets and benchmarks." Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. 2022. 组内的工作，有一个通用IAA数据集TAD66K

2021

Dietrich, Philip, and Thomas Knieper. “(Neuro)Aesthetics: Beauty, Ugliness, and Ethics.” PsyCh Journal, Aug. 2021, p. pchj.478. DOI.org (Crossref), https://doi.org/10.1002/pchj.478 (opens in a new tab).

2020及之前

鲁越, et al. "绘画艺术图像的计算美学: 研究前沿与展望." 自动化学报 46.11 (2020): 2239-2259. 这篇综述写的不错，推荐入门同学看一下
Talebi, Hossein, and Peyman Milanfar. "NIMA: Neural image assessment." IEEE transactions on image processing 27, no. 8 (2018): 3998-4011.https://ieeexplore.ieee.org/abstract/document/8352823 (opens in a new tab)

多因素美学评估

2023

Huang, Heng, et al. Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels. arXiv:2312.03222, arXiv, 5 Dec. 2023. arXiv.org, http://arxiv.org/abs/2312.03222.
- 电子科技大学那个组的论文，本来是投AAAI2022但是没有中，现时隔一年修改后放在arxiv，文章质量一般。
- Huang 等 - 2023 - Predicting Scores of Various Aesthetic Attribute S.pdf
- 文中提供了不少可能对单因素评估有参考价值的开源代码，其中还涉及人脸、动作等：
  - 分割模型：https://github.com/facebookresearch/MaskFormer (opens in a new tab)
  - 目标检测模型：https://github.com/facebookresearch/detr (opens in a new tab)
  - 太阳位置和物理天空、相机参数：https://github.com/PeterZhouSZ/dashcam-illumination-estimation (opens in a new tab)
  - 语义线检测：https://github.com/Hanqer/deep-hough-transform (opens in a new tab)
  - 深度估计：https://github.com/nianticlabs/monodepth2 (opens in a new tab)
  - 人脸光照：https://github.com/zhhoper/DPR (opens in a new tab)
  - 姿态估计：https://github.com/princeton-vl/pytorch_stacked_hourglass (opens in a new tab)

2022

Zhu, Hancheng, et al. “Learning Image Aesthetic Subjectivity from Attribute-Aware Relational Reasoning Network.” Pattern Recognition Letters, vol. 155, Mar. 2022, pp. 84–91. DOI.org (Crossref), https://doi.org/10.1016/j.patrec.2022.02.008 (opens in a new tab).
Celona, Luigi, et al. “Composition and Style Attributes Guided Image Aesthetic Assessment.” IEEE Transactions on Image Processing, vol. 31, 2022, pp. 5009–24. arXiv.org, https://doi.org/10.1109/TIP.2022.3191853 (opens in a new tab).
“Psychology Inspired Model for Hierarchical Image Aesthetic Attribute Prediction.” 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022, pp. 1–6. IEEE Xplore, https://doi.org/10.1109/ICME52920.2022.9859845 (opens in a new tab).
Li, Leida, et al. Psychology Inspired Model for Hierarchical Image Aesthetic Attribute Prediction. IEEE Computer Society, 2022, pp. 1–6. www.computer.org, https://doi.org/10.1109/ICME52920.2022.9859845 (opens in a new tab).

2021

Leonardi, Marco, et al. “Modeling Image Aesthetics through Aesthetics-Related Attributes.” London Imaging Meeting, vol. 2, no. 1, Sept. 2021, pp. 11–15. DOI.org (Crossref), https://doi.org/10.2352/issn.2694-118X.2021.LIM-11 (opens in a new tab).

2020及之前

Chen, Zhihong. ‘【多因素】Data Covariance Learning in Aesthetic Attributes Assessment’. Journal of Applied Mathematics and Physics, vol. 08, no. 12, 2020, pp. 2869–79, https://doi.org/10.4236/jamp.2020.812212 (opens in a new tab).
Reddy, Gajjala Viswanatha, et al. ‘【多因素】Measuring Photography Aesthetics with Deep CNNs’. IET Image Processing, vol. 14, no. 8, 2020, pp. 1561–70, https://doi.org/10.1049/iet-ipr.2019.1300 (opens in a new tab).
Shu, Yangyang, et al. ‘【多因素】Learning with Privileged Information for Photo Aesthetic Assessment’. Neurocomputing, vol. 404, 2020, pp. 304–16, https://doi.org/10.1016/j.neucom.2020.04.142 (opens in a new tab).
Baudin, Emilie, et al. “DXOMARK Objective Video Quality Measurements.” Electronic Imaging, vol. 32, no. 9, Jan. 2020, pp. 166-1-166–67. DOI.org (Crossref), https://doi.org/10.2352/ISSN.2470-1173.2020.9.IQSP-166 (opens in a new tab).
Kang, Chen, et al. “EVA: An Explainable Visual Aesthetics Dataset.” Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, ACM, 2020, pp. 5–13. DOI.org (Crossref), https://doi.org/10.1145/3423268.3423590 (opens in a new tab).
Pan, Bowen, et al. “Image Aesthetic Assessment Assisted by Attributes through Adversarial Learning.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, July 2019, pp. 679–86. DOI.org (Crossref), https://doi.org/10.1609/aaai.v33i01.3301679 (opens in a new tab).
Malu, Gautam. 【多因素】【高引】Learning Photography Aesthetics with Deep CNNs. 2017年.
Kong, Shu, et al. Photo Aesthetics Ranking Network with Attributes and Content Adaptation. arXiv:1606.01621, arXiv, 26 July 2016. arXiv.org, httphttp://arxiv.org/abs/1606.01621://arxiv.org/abs/1606.01621. AADB数据集，很重要
Aydin, Tunc Ozan, et al. “Automated Aesthetic Analysis of Photographic Images.” IEEE Transactions on Visualization and Computer Graphics, vol. 21, no. 1, Jan. 2015, pp. 31–42. DOI.org (Crossref), https://doi.org/10.1109/TVCG.2014.2325047 (opens in a new tab).
Lu, Xin, et al. “RAPID: Rating Pictorial Aesthetics Using Deep Learning.” Proceedings of the 22nd ACM International Conference on Multimedia, ACM, 2014, pp. 457–66. DOI.org (Crossref), https://doi.org/10.1145/2647868.2654927 (opens in a new tab).
Marchesotti, Luca, et al. Discovering Beautiful Attributes for Aesthetic Image Analysis. arXiv:1412.4940, arXiv, 16 Dec. 2014. arXiv.org, http://arxiv.org/abs/1412.4940.
Dhar, Sagnik, et al. ‘【多因素】High Level Describable Attributes for Predicting Aesthetics and Interestingness’. CVPR 2011, IEEE, 2011, pp. 1657–64, https://doi.org/10.1109/CVPR.2011.5995467 (opens in a new tab).

成果展示基础摄影原理