Journal of Infrared and Millimeter Waves, Volume. 42, Issue 6, 916(2023)
An unsupervised few-shot infrared aerial object recognition network based on deep-shallow learning graph model
[1] LI Kai-Yong, HE You-Jin, ZHANG Peng et al. A method for ground target recognition through IR imaging[J]. Electronics Optics& Control.
[2] Sun S G, Park H W. Automatic target recognition using boundary partitioning and invariant features in forward-looking infrared images[J]. Optical Engineering, 42, 524-533(2003).
[3] Pan S J, Tsang I W, Kwok J T et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 22, 199-210(2010).
[4] Tzeng E, Hoffman J, Zhang N et al. Deep domain confusion: maximizing for domain invariance[J](2014).
[5] Long M, Cao Y, Cao Z et al. Learning transferable features with deep adaptation networks: proceedings of the 32nd International Conference on Machine Learning, 2015[C], 97-105(2015).
[6] Ganin Y, Ustinova E, Ajakan H et al. Domain adversarial training of neural networks[J]. The Journal of Machine Learning Research, 17, 1-35(2016).
[7] Zhou Q, Zhou W, Wang S et al. Multiple adversarial networks for unsupervised domain adaptation[J]. Knowledge-Based Systems, 212, 106606(2021).
[8] Liu Z, Wang S, Zheng L et al. Robust imagegraph: rank-level feature fusion for image search[J]. IEEE Transactions on Image Processing, 26, 3128-3141(2017).
[9] HU Yang-Guang, XIAO Ming-Qing, ZHANG Kai et al. Infrared aerial target tracking based on fusion of traditional feature and deep feature[J]. Systems Engineering and Electronics.
[10] CHEN Yu, WEN Xin-Ling, LIU Zhao-Yu et al. Research of multi-missile classification algorithm based on sparse auto-encoder visual feature fusion[J]. Infrared and Laser Engineering.
[11] Yang W, Greg M. A discriminative latent model of object classes and attributes: 11th European Conference on Computer Vision, 2010[C], 155-168(2010).
[12] GONG Ping, CHENG Yu-Hu, WANG Xue-Song. Zero-shot classification based on attribute correlation graph regularized feature selection[J]. Journal of China University of Mining & Technology.
[13] Wang X, Ye Y, Gupta A. Zero-shot recognition via semantic embeddings and knowledge graphs: proceedings of the IEEE conference on computer vision and pattern recognition, 2018[C], 6857-6866(2018).
[14] Gulrajani I, Ahmed F, Arjovsky M et al. Improved training of wasserstein GANs: advances in neural information processing systems, 2017[C], 5768-5778(2017).
[15] Shen J, Qu Y, Zhang W et al. Wasserstein distance guided representation learning for domain adaptation: proceedings of the AAAI Conference on Artificial Intelligence, 2018[C], 4058-4065(2018).
[16] Long M, Wang J, Jordan M I. Deep transfer learning with joint adaptation networks: proceedings of the International Conference on Machine Learning, 2017[C], 3470-3479(2017).
[17] Krueger D, Caballero E, Jacobsen J H et al. Out-of-distribution generalization via risk extrapolation[J](2021).
[18] Na J, Han D, Chang H J et al. Contrastive vicinal space for unsupervised domain adaptation: 17th European Conference on Computer Vision, 2022[C], 92-110(2022).
Get Citation
Copy Citation Text
Yu-Ze LI, Yan ZHANG, Yu CHEN, Chun-Ling YANG. An unsupervised few-shot infrared aerial object recognition network based on deep-shallow learning graph model[J]. Journal of Infrared and Millimeter Waves, 2023, 42(6): 916
Category: Research Articles
Received: Dec. 29, 2022
Accepted: --
Published Online: Dec. 26, 2023
The Author Email: Yan ZHANG (zyhit@hit.edu.cn), Chun-Ling YANG (yangcl1@hit.edu.cn)