Chang Lu
- Yann LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. 1989. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541-551.Google Scholar
- Guosheng Lin, Chunhua Shen, Anton van den Hengel, and Ian Reid. 2016. Efficient piecewise training of deep structured models for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3194-3203.Google Scholar
- Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130.Google Scholar
- Fei Wang, Mengqing Jiang, Chen Qian, 2017. Residual attention network for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3156-3164.Google ScholarCross Ref
- Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, 86(11), 2278-2324.Google Scholar
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.Google ScholarDigital Library
- Karen Simonyan, and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556Google Scholar
- Yiming Zuo, Peishun Liu, Yaqi Tan, Zhaoxia Guo, and Ruichun Tang. 2020, October. An attention-based lightweight residual network for plant disease recognition. In 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). IEEE, 224-228.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778.Google ScholarCross Ref
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.Google Scholar
- Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. 2019. Dual attention network for scene segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3146-3154.Google ScholarCross Ref
- Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329.Google Scholar
- Sepp Hochreiter, and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation, 9(8): 1735-1780.Google Scholar
- Hugo Larochelle, and Geoffrey E. Hinton. 2010. Learning to combine foveal glimpses with a third-order Boltzmann machine. Advances in Neural Information Processing Systems, 23, 1243-1251.Google Scholar
- Kelvin Xu, Jimmy Ba, Ryan Kiros, 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the 32nd International Conference on Machine Learning, PMLR 37: 2048-2057Google Scholar
- Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. CBAM: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV), 3-19.Google ScholarDigital Library
- Drew Linsley, Dan Scheibler, Sven Eberhardt, and Thomas Serre. 2018. Global-and-local attention networks for visual recognition. arXiv preprint arXiv:1805.08819.Google Scholar
- Francois Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251-1258.Google ScholarCross Ref
- Andrew G. Howard, Menglong Zhu, Bo Chen, 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.Google Scholar
- Corrado Alessio, Animals-10 Dataset, Animal Pictures of 10 Different Categories Taken from Google Images. Accessed on: Dec. 20, 2020, [Online]. Available: https://www.kaggle.com/alessiocorrado99/animals10Google Scholar
- A. Krizhevsky, and G. Hinton. 2009. Learning multiple layers of features from tiny images. Accessed on: Dec. 25, 2020, [Online]. Available: https://www.cs.toronto.edu/∼kriz/cifar.htmlGoogle Scholar
- Yu Wang, Quan Zhou, Jia Liu, Jian Xiong, Guangwei Gao, Xiaofu Wu, and Longin Jan Latecki. 2019, September. LedNet: A lightweight encoder-decoder network for real-time semantic segmentation. In 2019 IEEE International Conference on Image Processing, 1860-1864.Google Scholar
- Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. arXiv preprint arXiv:1602.07360.Google Scholar
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 248-255.Google ScholarCross Ref
- Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. 2018. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6848-6856.Google ScholarCross Ref
- Gao Huang, Zhuang Li, Laurens van der Maaten, and Kilian Q. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700-4708.Google Scholar
- Mingxing Tan, and Quoc Le 2019, May. EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97: 6105-6114.Google Scholar
- Ashish Vaswani, 2017. Attention is all you need. In Advances in Neural Information Processing Systems, 5998-6008.Google Scholar
- Yuhui Yuan, Lang Huang, Jianyuan Guo, 2018. OCNet: Object context network for scene parsing. arXiv preprint arXiv:1809.00916.Google Scholar
- Ryo Hasegawa, Yutaro Iwamoto, and Yen-Wei Chen. 2020. Robust Japanese road sign detection and recognition in complex scenes using convolutional neural networks. Journal of Image and Graphics, 8(3): 59-66.Google ScholarCross Ref
- Mengting Liu, Guoying Liu, Yongge Liu, and Qingju Jiao. 2020. Oracle-bone inscription recognition based on deep convolutional neural network. Journal of Image and Graphics, 8(4): 114-119.Google Scholar
- Angie M. Ceniza, Tom Kalvin B. Archival, and Kate V. Bongo. 2018. Mobile application for recognizing text in degraded document images using optical character recognition with adaptive document image binarization. Journal of Image and Graphics, 6(1):44-47.Google ScholarCross Ref
- Jie Hu, Li Shen and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132-7141.Google ScholarCross Ref
- Haifeng Zhang and Shenjie Xu. 2016. The face recognition algorithms based on weighted LTP. Journal of Image and Graphics. 4.1:11-14.Google ScholarCross Ref
- Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249-256.Google Scholar
Index Terms
- ALResNet: Attention-Driven Lightweight Residual Network for Fast and Accurate Image Recognition
Index terms have been assigned to the content through auto-classification.
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