Fetching the paper…
Reading the bibliography…
In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets.
R. W. Sperry, M. S. Gazzaniga, and J. E. Bogen, “Interhemispheric relationships: the neocortical commissures; syndromes of hemisphere disconnection,” 1969
1969
Earlier work this paper cites.
N. Schweighofer and K. Doya, “Meta-learning in reinforcement learning.” Neural Networks , vol. 16, no. 1, pp. 5–9, 2003
2003
Earlier work this paper cites.
H. Tong, M. Li, H.-J. Zhang, J. He, and C. Zhang, “Classification of digital photos taken by photographers or home users,” in Pacific-Rim Conference on Multimedia . Springer, 2004, pp. 198–205
2004
Earlier work this paper cites.
R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Studying aesthetics in photographic images using a computational approach,” in European conference on computer vision . Springer, 2006, pp. 288–301
2006
Earlier work this paper cites.
Y. Luo and X. Tang, “Photo and video quality evaluation: Focusing on the subject,” in European Conference on Computer Vision . Springer, 2008, pp. 386–399
2008
Earlier work this paper cites.
W. Luo, X. Wang, and X. Tang, “Content-based photo quality assessment,” in 2011 International Conference on Computer Vision . IEEE, 2011, pp. 2206–2213
2011
Earlier work this paper cites.
M. Nishiyama, T. Okabe, I. Sato, and Y. Sato, “Aesthetic quality classification of photographs based on color harmony,” in CVPR 2011 . IEEE, 2011, pp. 33–40
2011
Earlier work this paper cites.
L. Marchesotti, F. Perronnin, D. Larlus, and G. Csurka, “Assessing the aesthetic quality of photographs using generic image descriptors,” in 2011 international conference on computer vision . IEEE, 2011, pp. 1784–1791
2011
Earlier work this paper cites.
O. Wu, W. Hu, and J. Gao, “Learning to predict the perceived visual quality of photos,” in 2011 International Conference on Computer Vision . IEEE, 2011, pp. 225–232
2011
Earlier work this paper cites.
N. Murray, L. Marchesotti, and F. Perronnin, “Ava: A large-scale database for aesthetic visual analysis,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2012, pp. 2408–2415
2012
Earlier work this paper cites.
X. Lu, Z. Lin, H. Jin, J. Yang, and J. Z. Wang, “Rapid: Rating pictorial aesthetics using deep learning,” in Proceedings of the 22nd ACM international conference on Multimedia , 2014, pp. 457–466
2014
Earlier work this paper cites.
Y. Kao, C. Wang, and K. Huang, “Visual aesthetic quality assessment with a regression model,” in 2015 IEEE International Conference on Image Processing (ICIP) . IEEE, 2015, pp. 1583–1587
2015
Cited alongside, same era.
X. Lu, Z. Lin, X. Shen, R. Mech, and J. Z. Wang, “Deep multi-patch aggregation network for image style, aesthetics, and quality estimation,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 990–998
2015
Cited alongside, same era.
2015
Cited alongside, same era.
S. Kong, X. Shen, Z. Lin, R. Mech, and C. Fowlkes, “Photo aesthetics ranking network with attributes and content adaptation,” in European Conference on Computer Vision . Springer, 2016, pp. 662–679
2016
Cited alongside, same era.
X. Jin, L. Wu, X. Zhou, G. Zhao, X. Zhang, X. Li, and S. Ge, “Predicting aesthetic radar map using a hierarchical multi-task network,” in Chinese Conference on Pattern Recognition and Computer Vision (PRCV) . Springer, 2018, pp. 41–50
2018
Later among the works it cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Later among the works it cites.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International Conference on Machine Learning . PMLR, 2019, pp. 6105–6114
2019
Later among the works it cites.
Q. Kuang, X. Jin, Q. Zhao, and B. Zhou, “Deep multimodality learning for uav video aesthetic quality assessment,” IEEE Transactions on Multimedia , vol. 22, no. 10, pp. 2623–2634, 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, “Meta-learning with memory-augmented neural networks,” in International conference on machine learning . PMLR, 2016, pp. 1842–1850
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” 2016
2016
Cited alongside, same era.
A. Brachmann and C. Redies, “Computational and experimental approaches to visual aesthetics,” Frontiers in computational neuroscience , vol. 11, p. 102, 2017
2017
Cited alongside, same era.
K.-Y. Chang, K.-H. Lu, and C.-S. Chen, “Aesthetic critiques generation for photos,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3514–3523
2017
Cited alongside, same era.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International Conference on Machine Learning . PMLR, 2017, pp. 1126–1135
2017
Cited alongside, same era.
X. Jin, X. Zhou, X. Li, X. Zhang, H. Sun, X. Li, and R. Liu, “Incremental learning of multi-tasking networks for aesthetic radar map prediction,” IEEE Access , vol. 7, pp. 183 647–183 655, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “Eca-net: Efficient channel attention for deep convolutional neural networks, 2020 ieee,” in CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , 2020
2020
Later among the works it cites.
Y. Fang, H. Zhu, Y. Zeng, K. Ma, and Z. Wang, “Perceptual quality assessment of smartphone photography,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3677–3686
2020
Later among the works it cites.
D. Liu, R. Puri, N. Kamath, and S. Bhattacharya, “Composition-aware image aesthetics assessment,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020, pp. 3569–3578
2020
Later among the works it cites.