[1] Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[C]. European Conference on Computer Vision, 2016: 21-37.
[2] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359.
[3] Wang Y Q, Yao Q M, Kwok J T, et al. Generalizing from a few examples: a survey on few-shot learning[J]. ACM Computing Surveys, 2020, 53(3): 1-34.
[4] Mitchell T M. Machine learning[M].New York: McGraw-Hill, 1997.
[5] Bottou L, Bousquet O. The tradeoffs of large scale learning[C].Proceedings of the 21^st Annual Conference on Neural Information Processing Systems, 2007: 161-168.
[6] Bottou L, Curtis F E, Nocedal J. Optimization methods for large-scale machine learning[J]. SIAM Review, 2016, 60(2): 223-311.
[7] Qi H, Brown M, Lowe D G. Low-shot learning with imprinted weights[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 5822-5830.
[8] Shyam P, Gupta S, Dukkipati A. Attentive recurrent comparators[C]. Proceedings of the 34^th International Conference on Machine Learning, 2017: 3173-3181.
[9] Lake B M, Salakhutdinov R, Tenenbaum J B. Human-level concept learning through probabilistic program induction[J]. Science, 2015, 350(6266): 1332-1338.
[10] Garcia V, Bruna J. Few-shot learning with graph neural networks[EB/OL]. https://arxiv.org/pdf/1711.04043.pdf.
[11] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C].IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[12] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/pdf/1804.02767.pdf.
[13] Redmon J, Farhadi A. YOLO9000: better, faster, stron-ger[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6517-6525.
[14] Hariharan B, Girshick R. Low-shot visual recognition by shrinking and hallucinating features[C].IEEE Internatio-nal Conference on Computer Vision, 2017: 3037-3046.
[15] Dwibedi D, Misra I, Hebert M. Cut, paste and learn: surprisingly easy synthesis for instance detection[C].IEEE International Conference on Computer Vision, 2017: 1310-1319.
[16] Lemley J, Bazrafkan S, Corcoran P. Smart augmentation learning an optimal data augmentation strategy[J]. IEEE Access, 2017, 5: 5858-5869.
[17] Zoph B, Cubuk E D, Ghiasi G, et al. Learning data augmentation strategies for object detection[C]. European Conference on Computer Vision, 2020: 566-583.
[18] Douze M, Szlam A, Hariharan B, et al. Low-shot learning with large-scale diffusion[C].IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 3349-3358.
[19] Wang X L, Shrivastava A, Gupta A, et al. A-Fast-RCNN: hard positive generation via adversary for object detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3039-3048.
[20] Ratner A J, Ehrenberg H R, Hussain Z, et al. Learning to compose domain—specific transformations for data augmentation[C]. Proceedings of the 31^st International Conference on Neural Information Processing Systems, 2017: 3239-3249.
[21] Tang P, Ramaiah C, Wang Y, et al. Proposal learning for semi-supervised object detection[EB/OL]. https://arxiv.org/pdf/2001.05086.pdf.
[22] Nguyen N V, Rigaud C, Burie J C. Semi-supervised object detection with unlabeled data[C]. 14^th International Conference on Computer Vision Theory and Applications, 2019: 289-296.
[23] Huang S W, Lin C T, Chen S P, et al. AugGAN: cross domain adaptation with GAN-based data augmentation[C]. European Conference on Computer Vision, 2018: 731-744.
[24] Caruana R. Multitask learning[J]. Machine Learning, 1997, 28: 41-75.
[25] Sermanet P, Eigen D, Zhang X, et al. OverFeat: integrated recognition, localization and detection using convolutional networks[EB/OL]. https://arxiv.org/pdf/1312.6229.pdf.
[26] Dong X Y, Zheng L, Ma F, et al. Few-example object detection with model communication[J]. IEEE Transac-tions on Pattern Analysis and Machine Intelligence, 2019, 41(7): 1641-1654.
[27] Luo Z L, Zou Y L, Hoffman J, et al. Label efficient learning of transferable representations across domains and tasks[C]. Proceedings of the 31^st International Conference on Neural Information Processing Systems, 2017: 164-176.
[28] Chabot F, Chaouch M, Rabarisoa J, et al. Deep MANTA: a coarse-to-fine many-task network for joint 2D and 3D vehicle analysis from monocular image[C].IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1827-1836.
[29] Zhang K P, Zhang Z P, Li Z F, et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 2016, 23(10): 1499-1503.
[30] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]. Proceedings of the 25^th International Conference on Neural Information Processing Systems, 2012: 1097-1105.
[31] Pérez-Rúa J M, Zhu X T, Hospedales T, et al. Incremental few-shot object detection[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 13843-13852.
[32] Zhou X Y, Wang D Q, Krähenbühl P. Objects as points[EB/OL]. https://arxiv.org/pdf/1904.07850.pdf.
[33] Peng C, Zhao K, Lovell B C. Faster ILOD: incremental learning for object detectors based on Faster RCNN[J]. Pattern Recognition Letters, 2020,140: 109-115.
[34] Shmelkov K, Schmid C, Alahari K. Incremental learning of object detectors without catastrophic forgetting[EB/OL]. IEEE International Conference on Computer Vision, 2017: 3420-3429.
[35] Li D W, Tasci S, Ghosh S, et al. RILOD: near real-time incremental learning for object detection at the edge[C]. Proceedings of the 4^th ACM/IEEE Symposium on Edge Computing, 2019: 113-126.
[36] Pan S J, Tsang I W, Kwok J T, et al. Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks, 2011, 22(2): 199-210.
[37] Kang B Y, Liu Z, Wang X, et al. Few-shot object detection via feature reweighting[C]. IEEE/CVF International Conference on Computer Vision, 2019: 8419-8428.
[38] Wang T, Zhang X P, Yuan L, et al. Few-shot adaptive Faster R-CNN[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 7166-7175.
[39] Zhang J Y, Chen Z L, Huang J Y, et al. Few-shot domain adaptation for semantic segmentation[C]. Proceedings of the ACM Turing Celebration Conference, 2019: 1-6.
[40] Motiian S, Jones Q, Iranmanesh S M, et al. Few-shot adversarial domain adaptation[C]. Proceedings of the 31^st International Conference on Neural Information Processing Systems, 2017: 6673-6683.
[41] Chen H, Wang Y, Wang G Y, et al. LSTD: a low-shot transfer detector for object detection[EB/OL]. https://arxiv.org/pdf/1803.01529.pdf.
[42] Schwartz E, Karlinsky L, Shtok J, et al. RepMet: representative-based metric learning for classification and one-shot object detection[EB/OL]. https://arxiv.org/pdf/1806.04728.pdf.
[43] Hao F S, Cheng J, Wang L, et al. Instance-Level embedding adaptation for few-shot learning[J]. IEEE Access, 2019, 7: 100501-100511..
[44] Musgrave K, Belongie S, Lim S N. A metric learning reality check[C]. European Conference on Computer Vision,2020: 681-699.
[45] Singh B, Davis L S. An analysis of scale invariance in object detectio—SNIP[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 3578-3587.
[46] Wang G T, Luo C, Sun X Y, et al. Tracking by instance detection: a Meta-learning approach[EB/OL]. https://arxiv.org/pdf/2004.00830.pdf.
[47] Fu K, Zhang T F, Zhang Y, et al. Meta-SSD: towards fast adaptation for few-shot object detection with Meta-learning[J]. IEEE Access, 2019, 7: 77597-77606.
[48] Yan X P, Chen Z L, Xu A, et al. Meta R-CNN : towards general solver for instance-level low-shot learning[EB/OL]. https://arxiv.org/pdf/1909.13032.pdf.
[49] Zhu X J, Goldberg A B. Introduction to semi-supervised learning[M]. San Rafael: Morgan and Claypool Publis-hers, 2009.
[50] He H B, Garcia E A. Learning from imbalanced data[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263-1284.
[51] Lampert C H, Nickisch H, Harmeling S. Learning to detect unseen object classes by between-class attribute transfer[C].IEEE Conference on Computer Vision and Pattern Recognition, 2009: 951-958.
[52] Franceschi L, Frasconi P, Salzo S, et al. Bilevel programming for hyperparameter optimization andMeta-learning[C]. Proceedings of the 35^th International Conference on Machine Learning, 2018: 1568-1577.
[53] Kirkpatricka J, Pascanu R, Rabinowitz N, et al. Overcoming catastrophic forgetting in neural networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(13): 3521-3526.
[54] Kanter J M, Veeramachaneni K. Deep feature synthesis: towards automating data science endeavors[C].IEEE International Conference on Data Science and Advanced Analytics, 2015: 717-726.
[55] Kotthoff L, Thornton C, Hoos H H, et al. Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA[J]. Journal of Machine Learning Research, 2017, 18(25): 1-5.
[56] Zoph B, Le Q V. Neural architecture search with reinforcement learning[C]. Proceedings of the 34^th Interna-tional Conference on Machine Learning, 2017: 459-468.
[57] Kaiser Ł, Nachum O, Roy A, et al. Learning to remem-ber rare events[EB/OL]. https://arxiv.org/pdf/1703.03129.pdf.