[1] Akçay S, Kundegski M E, Devereux M, et al. Transfer learning using convolutional neural wks f object classification within Xray baggage security imagery[C]Conference on International Conference on Image Processing, 2016: 1057–1061.
[2] Jaccard N, Rogers T W, Mton E J, et al. Tackling the Xray cargo inspection challenge using machine learning[C]Conference on Anomaly Detection Imaging with XRays. International Society f Optics Photonics, 2016, 9847: 98470N.
[3] Singh M, Singh S. Image segmentation optimisation f Xray images of airline luggage[C]Conference on Computational Intelligence f Homel Security Personal Safety, 2004: 1017.
[4] Bhowmik N, Gaus Y F A, Akçay S, et al. On the impact of object subcomponent level segmentation strategies f supervised anomaly detection within Xray security imagery[C]Conference on Machine Learning Applications, 2019: 986991.
[5] An J, Zhang H, Zhu Y, et al. Semantic segmentation f prohibited items in baggage inspection[C]Conference on Intelligent Science Big Data Engineering, 2019: 495505.
[6] N Yang, L Nan, D Y Zhang, et al. Research on image interpretation based on deep learning. Infrared and Laser Engineering, 47, 0203002(2018).
[7] C Finn, P Abbeel, S Levine. Model-agnostic meta-learning for fast adaptation of deep networks. arXiv preprint, arXiv1703.03400(2017).
[8] S Xue, Z Zhang, Q Y Lv, et al. Image recognition method of anti UAV system based on convolutional neural network. Infrared and Laser Engineering, 49, 20200154(2020).
[9] Snell J, Swersky K, Zemel R. Prototypical wks f fewshot learning[C]Conference on Advances in Neural Infmation Processing Systems, 2017: 40774087.
[10] Sung F, Yang Y, Zhang L, et al. Learning to compare: Relation wk f fewshot learning[C]Conference on Computer Vision Pattern Recognition, 2018: 11991208.
[11] A Shaban, S Bansal, Z Liu, et al. One-shot learning for semantic segmentation. arXiv preprint, arXiv1709.03410(2017).
[12] Rakelly K, Shelhamer E, Darrell T, et al. Conditional wks f fewshot semantic segmentation[C]Conference on Learning Representations Wkshop, 2018.
[13] Zhang C, Lin G, Liu F, et al. Ca: Classagnostic segmentation wks with iterative refinement attentive fewshot learning[C]Conference on Computer Vision Pattern Recognition, 2019: 52175226.
[14] Y Li, C Gu, T Dullien, et al. Graph matching networks for learning the similarity of graph structured objects. arXiv preprint, arXiv1904.12787(2019).
[15] Sarlin P E, DeTone D, Malisiewicz T, et al. Superglue: Learning feature matching with graph neural wks[C]Conference on Computer Vision Pattern Recognition, 2020: 49384947.
[16] Zhang C, Lin G, Liu F, et al. Pyra graph wks with connection attentions f regionbased oneshot semantic segmentation[C]Proceedings of the IEEE International Conference on Computer Vision, 2019: 95879595.
[17] Zhang C, Cai Y, Lin G, et al. DeepEMD: Fewshot image classification with differentiable earth mover’s distance structured classifiers[C]Conference on Computer Vision Pattern Recognition, 2020: 1220012210.
[18] Zeiler M D, Fergus R. Visualizing understing convolutional wks[C]European Conference on Computer Vision, 2014: 818833.
[19] He K, Zhang X, Ren S, et al. Deep residual learning f image recognition[C]Conference on Computer Vision Pattern Recognition, 2016: 770778.
[20] Rubner Y, Tomasi C, Guibas L J. A metric f distributions with applications to image databases[C]Proceedings of the IEEE International Conference on Computer Vision, 1998: 5966.
[21] L C Chen, G Papandreou, F Schroff, et al. Rethinking atrous convolution for semantic image segmentation. arXiv preprint, arXiv1706.05587(2017).
[22] Milletari F, Navab N, Ahmadi S A. V: Fully convolutional neural wks f volumetric medical image segmentation[C]International Conference on 3D Vision (3DV). IEEE, 2016: 565571.
[23] Y Piao, L Liu, X Y Liu. Enhancement technology of video under low illumination. Infrared and Laser Engineering, 43, 2021-2026(2014).
[24] Miao C, Xie L, Wan F, et al. Sixray: A largescale security inspection xray benchmark f prohibited item discovery in overlapping images[C]Conference on Computer Vision Pattern Recognition, 2019: 21192128.