[1] Li J Y, Li X R, Zhao L Y. Ship target detection in optical remote sensing images based on spatial and frequency features[J]. Laser & Optoelectronics Progress, 58, 0415005(2021).

[2] Xu Z J, Ding Y. Ship object detection of remote sensing images based on adaptive rotation region proposal network[J]. Laser & Optoelectronics Progress, 57, 242805(2020).

[3] Vinyals O, Blundell C, Lillicrap T et al. Matching networks for one shot learning[C], 3630-3638(2016).

[4] Fu K, Zhang T F, Zhang Y et al. OSCD: a one-shot conditional object detection framework[J]. Neurocomputing, 425, 243-255(2021).

[5] Chen H, Wang Y L, Wang G Y et al. LSTD: a low-shot transfer detector for object detection[EB/OL]. https://arxiv.org/abs/1803.01529

[6] Fan Q, Zhuo W, Tang C K et al. Few-shot object detection with attention-RPN and multi-relation detector[C], 4012-4021(2020).

[7] Karlinsky L, Shtok J, Harary S et al. RepMet: representative-based metric learning for classification and few-shot object detection[C], 5192-5201(2019).

[8] Wang T, Zhang X P, Yuan L et al. Few-shot adaptive faster R-CNN[C], 7166-7175(2019).

[9] 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, 7, 77597-77606(2019).

[10] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[11] Ben-David S, Blitzer J, Crammer K et al. A theory of learning from different domains[J]. Machine Learning, 79, 151-175(2010).

[12] Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation[C], 1180-1189(2015).

[13] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[14] Cordts M, Omran M, Ramos S et al. The cityscapes dataset for semantic urban scene understanding[C], 3213-3223(2016).

[15] Sakaridis C, Dai D X, van Gool L. Semantic foggy scene understanding with synthetic data[J]. International Journal of Computer Vision, 126, 973-992(2018).

[16] Everingham M, van Gool L, Williams C K I et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 88, 303-338(2010).

[17] Inoue N, Furuta R, Yamasaki T et al. Cross-domain weakly-supervised object detection through progressive domain adaptation[C], 5001-5009(2018).

[18] Bertinetto L, Valmadre J, Henriques J F et al. Fully-convolutional Siamese networks for object tracking[M]. Hua G, Jégou H. Computer vision-ECCV 2016 workshops. Lecture notes in computer science, 9914, 850-865(2016).

[19] Li B, Yan J J, Wu W et al. High performance visual tracking with Siamese region proposal network[C], 8971-8980(2018).

[20] Hsieh T I, Lo Y C, Chen H T et al. One-shot object detection with co-attention and co-excitation[EB/OL]. https://arxiv.org/abs/1911.12529v1