[1] |
Yao A, Shao J, Ma N, et al. Capturing au-aware facial features and their latent relations for emotion recognition in the wild[C]//Proceedings of the ACM International Conference on Multimodal Interaction, 2015: 451-458. |
[2] |
Kang D, Shim H, Yoon K. A method for extracting emotion using colors comprise the painting image [J]. Multimedia Tools and Applications, 2018, 77(4): 4985-5002. doi: 10.1007/s11042-017-4667-0 |
[3] |
Song K, Yao T, Ling Q, et al. Boosting image sentiment analysis with visual attention [J]. Neurocomputing, 2018, 312: 218-228. doi: 10.1016/j.neucom.2018.05.104 |
[4] |
He X, Zhang W. Emotion recognition by assisted learning with convolutional neural networks [J]. Neurocomputing, 2018, 291: 187-194. doi: 10.1016/j.neucom.2018.02.073 |
[5] |
Castelvecchi D. Can we open the black box of AI? [J]. Nature, 2016, 538(7623): 20-23. doi: 10.1038/538020a |
[6] |
Voosen P. How AI detectives are cracking open the black box of deep learning[EB/OL]. [2019-09-12]. https://doi.org/10.1126/science.aan7059. |
[7] |
Fu R, Li B, Gao Y, et al. Visualizing and analyzing convolution neural networks with gradient information [J]. Neurocomputing, 2018, 293: 12-17. doi: 10.1016/j.neucom.2018.02.080 |
[8] |
Olah C, Mordvintsev A, Schubert L. Feature visualization[C]// IEEE Transactions on Visualization and Computer Graphics, 2020, 26(1): 2034594. |
[9] |
Olah C, Satyanarayan A, Johnson I, et al. The building blocks of interpretability [J]. Distill, 2018: 00010. |
[10] |
Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2921-2929. |
[11] |
Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization[C]//Proceedings of the International Conference on Computer Vision, 2017: 618-626. |
[12] |
Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[C]//Proceedings of the European Conference on Computer Vision, 2014: 818-833. |
[13] |
Fong R, Vedaldi A. Interpretable explanations of black boxes by meaningful perturbation[C]//Proceedings of the International Conference on Computer Vision, 2017: 3449-3457. |
[14] |
Li Yuzhi, Sheng Jiachuan, Hua Bin. Improved embedded learning for classification of Chinese paintings [J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(5): 893-900. (in Chinese |
[15] |
Sheng Jiachuan, Li Yuzhi. Learning artistic objects for improved classification of Chinese paintings [J]. Journal of Image and Graphics, 2018, 23(8): 1193-1206. (in Chinese |
[16] |
Liu Pengfei, Zhao Huaici, Cao Feidao. Blind deblurring of noisy and blurry images of multi-scale convolutional neural network [J]. Infrared and Laser Engineering, 2019, 48(4): 0426001. (in Chinese doi: 10.3788/IRLA201948.0426001 |
[17] |
Fang Shengnan, Gu Xiaojing, Gu Xingsheng. Infrared target tracking with correlation filter based on adaptive fusion of responses [J]. Infrared and Laser Engineering, 2019, 48(6): 0626003. (in Chinese doi: 10.3788/IRLA201948.0626003 |
[18] |
Hu Shanjiang, He Yan, Tao Bangyi, et al. Classification of sea and land waveforms with deep learning for airborne laser bathymetry [J]. Infrared and Laser Engineering, 2019, 48(11): 1113004. (in Chinese doi: 10.3788/IRLA201948.1113004 |
[19] |
Sheng J, Li Y. Classification of traditional Chinese paintings using a modified embedding algorithm [J]. Journal of Electronic Imaging, 2019, 28(2): 023013. |
[20] |
Sheng J, Song C, Wang J, et al. Convolutional neural network style transfer towards chinese paintings[C]//IEEE Access, 2019, 7: 163719-163728. |
[21] |
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9. |
[22] |
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the International Conference on Learning Representations, 2015: 1-14. |
[23] |
Erhan D, Bengio Y, Courville A, et al. Visualizing higher-layer features of a deep network[D]. Canada: University of Montreal, 2009. |
[24] |
Lin M, Chen Q, Yan S. Network in network[C]//Proceedings of the International Conference on Learning Representations, 2014: 1-10. |
[25] |
You Q, Luo J, Jin H, et al. Robust image sentiment analysis using progressively trained and domain transferred deep networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2015: 381-388. |