1 |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]// Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2012:1097-1105.
|
2 |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2021-02-17].. 10.5244/c.28.6
|
3 |
LIN M, CHEN Q, YAN S C. Network in network[EB/OL]. (2015-04-10) [2021-02-17].. 10.1109/icicta.2014.118
|
4 |
MURRAY N, PERRONNIN F. Generalized max pooling[C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2014:2473-2480. 10.1109/cvpr.2014.317
|
5 |
XIE G S, ZHANG X Y, SHU X B, et al. Task-driven feature pooling for image classification[C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015:1179-1187. 10.1109/iccv.2015.140
|
6 |
WU M X, CHENG G, YAO X W, et al. Performance comparison of two pooling strategies for remote sensing image scene classification[C]// Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium. Piscataway: IEEE, 2019: 3037-3040. 10.1109/igarss.2019.8899877
|
7 |
HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916. 10.1109/tpami.2015.2389824
|
8 |
LIN T Y, RoyCHOWDHURY A, MAJI S. Bilinear CNN models for fine-grained visual recognition[C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015:1449-1457. 10.1109/iccv.2015.170
|
9 |
LI P H, XIE J T, WANG Q L, et al. Is second-order information helpful for large-scale visual recognition?[C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017:2089-2097. 10.1109/iccv.2017.228
|
10 |
LI P H, XIE J T, WANG Q L, et al. Towards faster training of global covariance pooling networks by iterative matrix square root normalization[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018:947-955. 10.1109/cvpr.2018.00105
|
11 |
WANG Q L, GAO Z L, XIE J T, et al. Global gated mixture of second-order pooling for improving deep convolutional neural networks[C]// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2018:1284-1293.
|
12 |
KIM J H, JUN J, ZHANG B T. Bilinear attention networks[C]// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2018:1571-1581.
|
13 |
HE N J, FANG L Y, LI Y, et al. High-order self-attention network for remote sensing scene classification[C]// Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium. Piscataway: IEEE, 2019: 3013-3016. 10.1109/igarss.2019.8898320
|
14 |
薛永杰,巨志勇. 注意力机制融合深度神经网络的室内场景识别方法[J]. 小型微型计算机系统, 2021, 42(5): 1022-1028. 10.3969/j.issn.1000-1220.2021.05.021
|
|
XUE Y J, JU Z Y. Method for recognizing indoor scene classification based on fusion deep neural network with attention mechanism[J]. Journal of Chinese Computer Systems, 2021, 42(5):1022-1028. 10.3969/j.issn.1000-1220.2021.05.021
|
15 |
边小勇,江沛龄,赵敏,等. 基于多分支神经网络模型的弱监督细粒度图像分类方法[J]. 计算机应用, 2020, 40(5):1295-1300.
|
|
BIAN X Y, JIANG P L, ZHAO M, et al. Multi-branch neural network model based weakly supervised fine-grained image classification method[J]. Journal of Computer Applications, 2020, 40(5):1295-1300.
|
16 |
LIN T Y, MAJI S. Improved bilinear pooling with CNNs[C]// Proceedings of the 2017 British Machine Vision Conference. Durham: BMVA Press, 2017: No.117. 10.5244/c.31.117
|
17 |
ZHAO Z Y, ZHANG K R, HAO X J, et al. BiRA-Net: bilinear attention net for diabetic retinopathy grading[C]// Proceedings of the 2019 IEEE International Conference on Image Processing. Piscataway: IEEE, 2019:395-399. 10.1109/icip.2019.8803074
|
18 |
XIA G S, HU J W, HU F, et al. AID: a benchmark data set for performance evaluation of aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7):3965-3981. 10.1109/tgrs.2017.2685945
|
19 |
CHENG G, HAN J W, LU X Q. Remote sensing image scene classification: benchmark and state of the art[J]. Proceedings of the IEEE, 2017, 105(10):1865-1883. 10.1109/jproc.2017.2675998
|
20 |
KRIZHEVSKY A. Learning multiple layers of features from tiny images[R/OL]. (2009-04-08) [2021-02-17]..
|
21 |
HE K M, ZHANG X Y, REN S Q. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016:770-778. 10.1109/cvpr.2016.90
|
22 |
HE N J, FANG L Y, LI S T, et al. Skip-connected covariance network for remote sensing scene classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(5): 1461-1474. 10.1109/tnnls.2019.2920374
|
23 |
ZAGORUYKO S, KOMODAKIS N. Wide residual networks[C]// Proceedings of the 2016 British Machine Vision Conference. Durham: BMVA Press, 2016: No.87. 10.5244/c.30.87
|
24 |
ZHONG X, GONG O B, HUANG W X, et al. Squeeze and excitation wide residual networks in image classification[C]// Proceedings of the 2019 IEEE International Conference on Image Processing. Piscataway: IEEE, 2019: 395-399. 10.1109/icip.2019.8803000
|
25 |
LUAN S Z, CHEN C, ZHANG B C, et al. Gabor convolutional networks[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4357-4366. 10.1109/tip.2018.2835143
|