[1] [1] 杜梅， 李景景. 复杂背景遥感影像中小目标船舶细粒度智能识别［J］. 舰船科学技术， 2021， 43（20）： 205-207. doi: 10.3404/j.issn.1672-7649.2021.10A.069DUM， LIJ J. Fine-grained intelligent recognition of small target ships in remote sensing images with complex background［J］. Ship Science and Technology， 2021， 43（20）： 205-207.（in Chinese）. doi: 10.3404/j.issn.1672-7649.2021.10A.069

[2] [2] 刘忻伟， 朴永杰， 郑亮亮， 等. 面向航天光学遥感复杂场景图像的舰船检测［J］. 光学 精密工程， 2023， 31（6）： 892-904. doi: 10.37188/OPE.20233106.0892LIUX W， PIAOY J， ZHENGL L， et al. Ship detection for complex scene images of space optical remote sensing［J］. Opt. Precision Eng.， 2023， 31（6）： 892-904.（in Chinese）. doi: 10.37188/OPE.20233106.0892

[3] [3] 肖术明， 张叶， 常旭岭， 等. 面向航天光学遥感场景压缩感知测量值的舰船检测［J］. 光学 精密工程， 2023， 31（4）： 517-532. doi: 10.37188/OPE.20233104.0517XIAOS M， ZHANGY， CHANGX L， et al. Ship detection oriented to compressive sensing measurements of space optical remote sensing scenes［J］. Opt. Precision Eng.， 2023， 31（4）： 517-532.（in Chinese）. doi: 10.37188/OPE.20233104.0517

[4] LIN T Y, ROYCHOWDHURY A, MAJI S. Bilinear CNN Models for Fine-Grained Visual Recognition[C], 1449-1457(7).

[5] FU J L, ZHENG H L, MEI T. Look closer to see better： recurrent attention convolutional neural network for fine-grained image recognition[C], 4476-4484(21).

[6] CHEN Y, BAI Y L, ZHANG W et al. Destruction and construction learning for fine-grained image recognition[C], 5152-5161(15).

[7] KODAMA Y, WANG Y N, KAWAKAMI R et al. Open-set recognition with supervised contrastive learning[C], 1-5(25).

[8] DAI W, DIAO W H, SUN X et al. CAMV： class activation mapping value towards open set fine-grained recognition[J]. IEEE Access, 9, 8167-8177(2021).

[9] CAI Z W, VASCONCELOS N. Cascade R-CNN： high quality object detection and instance segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 1483-1498(2021).

[10] VENDRAMINI M, OLIVEIRA H, MACHADO A et al. Opening deep neural networks with generative models[C], 1314-1318(19).

[11] YANG Y, HOU C, LANG Y et al. Open-set human activity recognition based on micro-Doppler signatures[J]. Pattern Recognition, 85, 60-69(2019).

[12] [12] 张婷. 基于注意力机制的图像内容理解与视觉推理算法研究［D］. 成都： 电子科技大学， 2021.ZHANGT. Research on Image Content Understanding and Visual Reasoning Algorithm Based on Attention Mechanism［D］. Chengdu： University of Electronic Science and Technology of China， 2021. （in Chinese）

[13] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[14] WANG Q L, WU B G, ZHU P F et al. ECA-Net： efficient channel attention for deep convolutional neural networks[C], 11531-11539(13).

[15] PARK J, LEE J Y et al. CBAM： Convolutional Block Attention Module[M]. Computer Vision-ECCV 2018, 3-19(2018).

[16] JADERBERG M, SIMONYAN K, ZISSERMAN A et al. Spatial Transformer Networks[webpage]. arXiv, 1506-02025(2015). https：//arxiv.org/abs/1506.02025.pdf

[17] ZHANG H L, XU H, LIN T E. Deep open intent classification with adaptive decision boundary[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 14374-14382(2021).

[18] WEN Y D, ZHANG K P, LI Z F et al. A Discriminative Feature Learning Approach for Deep Face Recognition[M]. Computer Vision - ECCV 2016, 499-515(2016).

[19] XU X W, ZHANG X L, ZHANG T W. Multi-Scale SAR Ship Classification with Convolutional Neural Network[C], 4284-4287(11).

[20] LIU Z W, MIAO Z Q, ZHAN X H et al. Large-scale long-tailed recognition in an open world[C], 2532-2541(15).

[21] LUO X, YU Z J, ZHAO Z G et al. Effective short text classification via the fusion of hybrid features for IoT social data[J]. Digital Communications and Networks, 942-954(2022).

[22] DI Y H, JIANG Z G, ZHANG H P. A public dataset for fine-grained ship classification in optical remote sensing images[J]. Remote Sensing, 13, 747(2021).

[23] GENG C X, HUANG S J, CHEN S C. Recent advances in open set recognition： a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 3614-3631(2021).