面向智能感知的小样本学习研究综述

doi:10.7527/S1000-6893.2019.23756

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

面向智能感知的小样本学习研究综述

宋闯¹, 赵佳佳¹, 王康², 梁欣凯¹

1. 复杂系统控制与智能协同技术重点实验室, 北京 100074;
2. 复旦大学计算机科学技术学院, 上海 200433

收稿日期:2019-12-13 修回日期:2019-12-20 出版日期:2020-06-30 发布日期:2019-12-26
通讯作者: 赵佳佳 E-mail:40068986@qq.com
基金资助:
国防基础科研计划（JCKY2017204B064）

A survey of few shot learning based on intelligent perception

SONG Chuang¹, ZHAO Jiajia¹, WANG Kang², LIANG Xinkai¹

1. Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing 100074, China;
2. School of Computer and Technique, Fudan University, Shanghai 200433, China

Received:2019-12-13 Revised:2019-12-20 Online:2020-06-30 Published:2019-12-26
Supported by:
Defense Industrial Technology Development Program (JCKY2017204B064)

摘要/Abstract

摘要： 小样本学习指只利用目标类别的少量监督信息来训练机器学习模型。由于其实用价值，学术界和工业界提出很多针对该问题的解决方案，但是目前国内缺少该问题的综述。本文对国内外学者提出的小样本学习算法及基于小样本学习的目标检测算法进行了系统的总结和探索。首先，给出了小样本学习的问题定义，列举其与其他一些经典的机器学习问题之间的联系，同时从理论上阐述小样本学习问题面临的挑战；接着，对基于小样本学习的图像分类进行了概述，并对其中代表性的工作进行介绍与分析；在此基础上，重点针对基于小样本学习的目标检测，特别是零样本条件下的目标检测问题，详细介绍和分析了现有的研究工作；最后，立足于现有方法的优缺点，从问题设定、理论研究、实现技术以及应用场景等几个方面对小样本学习的未来发展进行了展望，期望为该领域后续的研究工作提供启示。

关键词: 小样本学习, 机器学习, 图像分类, 目标检测, 零样本学习

Abstract: Few-shot learning refers to using only a small amount of supervision information of the target class to train the machine learning model. Due to its practical values, recent advances in few-shot learning by academia and industry have made significant contributions. However, there were few reviews on this issue in China. This paper systematically summarizes and explores the few-shot learning algorithms and the object detection algorithms based on few-shot learning. Firstly, the problem definition of few-shot learning is given, and its connections with other classic machine learning problems are also enumerated. Meanwhile, the theoretical challenges of the problem of few-shot learning are explained. Then, we summarize the image classification based on few-shot learning, and analyze its representative works. Based on this, we focus on the problem of few-shot object detection, especially the problem of zero-shot object detection, and analyze the existing research works in detail. Finally, we look forward to the future development of few-shot learning in terms of problem setting, theoretical research, implementation technology, and application scenarios based on the advantages and disadvantages of the existing methods. It is expected to provide inspirations for the subsequent research works in this field.

Key words: few-shot learning, machine learning, image classification, object detection, zero-shot learning

中图分类号:

宋闯, 赵佳佳, 王康, 梁欣凯. 面向智能感知的小样本学习研究综述[J]. 航空学报, 2020, 41(S1): 723756-723756.

SONG Chuang, ZHAO Jiajia, WANG Kang, LIANG Xinkai. A survey of few shot learning based on intelligent perception[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S1): 723756-723756.

参考文献 99

[1]	DENG J, DONG W, SOCHER R, et al. ImageNet:A large-scale hierarchical image database[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2009.
[2]	KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012.
[3]	HE K, ZHANG X, REN S. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016.
[4]	LONG J, EVAN S, TREVOR D. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2015.
[5]	REN S, HE K, GIRSHICK R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 39(6):1137-1149.
[6]	HOU X, ZHANG L. Saliency detection:A spectral residual approach[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2007.
[7]	WANG Y, GIRSHICK R, HEBERT M, et al. Low-shot learning from imaginary data[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018.
[8]	LAMPERT C H, NICKISCH H, HARMELING S. Attribute-based classification for zero-shot visual object categorization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 36(3):453-465.
[9]	SEBASTIAN T, PRATT L. Learning to learn[M]. Norwell:Springer Science & Business Media, 2012.
[10]	SCHAAL S. Is imitation learning the route to humanoid robots[J]. Trends in Cognitive Sciences, 1999, 3(6):233.
[11]	GARCIA V, BRUNA J. Few-shot learning with graph neural networks[EB/OL]. (2018-02-20)[2019-11-20]. https://arxiv.org/abs/1711.04043v1.
[12]	DUAN Y, ANDRYCHOWICZ M, STADIE B, et al. One-shot imitation learning[C]//Advances in Neural Information Processing Systems, 2017:1087-1098.
[13]	ORESHKIN B, LÓPEZ P R, LACOSTE A. Tadam:Task dependent adaptive metric for Improved mproved few-shot learning[C]//Advances in Neural Information Processing Systems, 2018:721-731.
[14]	REN M, TRIANTAFILLOU E, RAVI S, et al. Meta-learning for semi-supervised few-shot classification[EB/OL]. (2018-02-02)[2019-11-20]. https://arXivpreprintarXiv:1803.00676.
[15]	ROMERA-PAREDES B, TORR P. An embarrassingly simple approach to zero-shot learning[C]//International Conference on Machine Learning, 2015:2152-2161.
[16]	CHANGPINYO S, CHAO W L, GONG B, et al. Synthesized classifiers for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016:5327-5336.
[17]	KODIROV E, XIANG T, FU Z, et al. Unsupervised domain adaptation for zero-shot learning[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015:2452-2460.
[18]	ZHANG Z, SALIGRAMA V. Zero-shot learning via joint latent similarity embedding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016:6034-6042.
[19]	PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 22(10):1345-1359.
[20]	TURK M A, PENTLAND A P. Face recognition using eigenfaces[C]//Proceedings of 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 1991:586-591.
[21]	KINGMA D P, MOHAMED S, REZENDE D J, et al. Semi-supervised learning with deep generative models[C]//Advances in Neural Information Processing Systems, 2014:3581-3589.
[22]	BUCCINO G, VOGT S, RITZL A, et al. Neural circuits underlying imitation learning of hand actions:An event-related fMRI study[J]. Neuron, 2004, 42(2):323-334.
[23]	SMEULDERS A W M, WORRING M, SANTINI S, et al. Content-based image retrieval at the end of the early years[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2000(12):1349-1380.
[24]	BLACKMAN S. Multiple-target tracking with radar applications[M]. Dedham:Artech House, Inc., 1986.
[25]	FREEMAN W T, ROTH M. Orientation histograms for hand gesture recognition[C]//International Workshop on Automatic Face and Gesture Recognition, 1995:296-301.
[26]	XU K, BA J, KIROS R, et al. Show, attend and tell:Neural image caption generation with visual attention[C]//International Conference on Machine Learning, 2015:2048-2057.
[27]	ANTOL S, AGRAWAL A, LU J, et al. Vqa:Visual question answering[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015:2425-2433.
[28]	MEDIONI G, COHEN I, BRÉMOND F, et al. Event detection and analysis from video streams[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(8):873-889.
[29]	BENGIO Y, DUCHARME R, VINCENT P, et al. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3(2):1137-1155.
[30]	ZOPH B, LE Q V. Neural architecture search with reinforcement learning[EB/OL]. (2016-11-5)[2019-11-20]. https://arxiv.xilesou.top/pdf/1611.01578.
[31]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017:2980-2988.
[32]	WANG Y, YAO Q. Few-shot learning:A survey[EB/OL]. (2019-04-10)[2019-03-13]. https://arxiv.xilesou.top/pdf/1904.05046.
[33]	RUSSAKOVSKY O, LI F F. Attribute learning in large-scale datasets[C]//European Conference on Computer Vision. Heidelberg:Springer, 2010:1-14.
[34]	VILALTA R, DRISSI Y. A perspective view and survey of meta-learning[J]. Artificial Intelligence Review, 2002, 18(2):77-95.
[35]	KODIROV E, XIANG T, FU Z, et al. Unsupervised domain adaptation for zero-shot learning[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015:2452-2460.
[36]	ZHU X, GOLDBERG A B. Introduction to semi-supervised learning[J]. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2009, 3(1):1-130.
[37]	BARLOW H B. Unsupervised learning[J]. Neural Computation, 1989, 1(3):295-311.
[38]	ROSENBERG C, HEBERT M, SCHNEIDERMAN H. Semi-supervised self-training of object detection models[C]//2005 Seventh IEEE Workshops on Applications of Computer Vision. Piscataway:IEEE Press, 2005.
[39]	ZHOU Z H. A brief introduction to weakly supervised learning[J]. National Science Review, 2018, 5(1):44-53.
[40]	TORREY L, SHAVLIK J. Transfer learning[M]//Handbook of research on machine learning applications and trends:Algorithms, methods, and techniques. Hershey:IGI Global, 2009:242-264.
[41]	BROWN A L, CAMPIONE J C, DAY J D. Learning to learn:On training students to learn from texts[J]. Educational Researcher, 1981, 10(2):14-21.
[42]	HOCHREITER S, YOUNGER A S, CONWELL P R. Learning to learn using gradient descent[C]//International Conference on Artificial Neural Networks. Heidelberg:Springer, 2001:87-94.
[43]	PALATUCCI M, POMERLEAU D, HINTON G E, et al. Zero-shot learning with semantic output codes[C]//Advances in Neural Information Processing Systems, 2009:1410-1418.
[44]	ZHANG Z, SALIGRAMA V. Zero-shot learning via semantic similarity embedding[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015:4166-4174.
[45]	CHANGPINYO S, CHAO W L, GONG B, et al. Synthesized classifiers for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016:5327-5336.
[46]	KODIROV E, XIANG T, GONG S. Semantic autoencoder for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017:3174-3183.
[47]	ZHANG T, JOHNSON D. A robust risk minimization based named entity recognition system[C]//Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003-Volume 4, 2003:204-207.
[48]	CHEN W Y, LIU Y C, KIRA Z, et al. A closer look at few-shot classification[EB/OL]. (2019-04-08)[2019-01-12]. https://arxiv.xilesou.top/pdf/1904.04232.
[49]	VAN DYK D A, MENG X L. The art of data augmentation[J]. Journal of Computational and Graphical Statistics, 2001, 10(1):1-50.
[50]	HARIHARAN B, GIRSHICK R. Low-shot visual recognition by shrinking and hallucinating features[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017:3018-3027.
[51]	CHEN Z, FU Y, ZHANG Y, et al. Multi-level semantic feature augmentation for one-shot learning[J]. IEEE Transactions on Image Processing, 2019, 28(9):4594-4605.
[52]	HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[53]	GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems, 2014:2672-2680.
[54]	PERARNAU G, VAN DE WEIJER J, RADUCANU B, et al. Invertible conditional gans for image editing[EB/OL].[2016-11-19]. https://arxiv.xilesou.top/pdf/1611.06355.
[55]	CHU C, ZHMOGINOV A, SANDLER M. Cyclegan, a master of steganography[EB/OL].(2017-12-08)[2017-12-16]. https://arxiv.xilesou.top/pdf/1712.02950.
[56]	HOSSEINI-ASL E, ZHOU Y, XIONG C, et al. Augmented cyclic adversarial learning for low resource domain adaptation[EB/OL]. (2018-07-01)[2019-01-23]. https://arxiv.xilesou.top/pdf/1807.00374.
[57]	JUANG B H, RABINER L R. Hidden Markov models for speech recognition[J]. Technometrics, 1991, 33(3):251-272.
[58]	CHEN Z, FU Y, CHEN K, et al. Image block augmentation for one-shot learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019:3379-3386.
[59]	CHEN Z, FU Y, WANG Y X, et al. Image deformation meta-networks for one-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019:8680-8689.
[60]	VINYALS O, BLUNDELL C, LILLICRAP T, et al. Matching networks for one shot learning[C]//Advances in Neural Information Processing Systems, 2016:3630-3638.
[61]	SNELL J, SWERSKY K, ZEMEL R. Prototypical networks for few-shot learning[C]//Advances in Neural Information Processing Systems, 2017:4077-4087.
[62]	SUNG F, YANG Y, ZHANG L, et al. Learning to compare:Relation network for few-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018:1199-1208.
[63]	BERTINETTO L, HENRIQUES J F, TORR P H S, et al. Meta-learning with differentiable closed-form solvers[EB/OL]. (2018-05-21)[2019-12-20]. https://arxiv.xilesou.top/pdf/1805.08136.
[64]	GARCIA V, BRUNA J. Few-shot learning with graph neural networks[EB/OL]. (2017-11-10)[2019-12-20]. https://arxiv.xilesou.top/pdf/1711.04043.
[65]	HUANG Z, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[EB/OL]. (2015-08-09)[2019-12-20]. https://arxiv.xilesou.top/pdf/1508.01991.
[66]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems, 2017:5998-6008.
[67]	QI H, BROWN M, LOWE D G. Low-shot learning with imprinted weights[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018:5822-5830.
[68]	GIDARIS S, KOMODAKIS N. Dynamic few-shot visual learning without forgetting[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018:4367-4375.
[69]	MOTIIAN S, PICCIRILLI M, ADJEROH D A, et al. Unified deep supervised domain adaptation and generalization[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017:5715-5725.
[70]	MOTIIAN S, JONES Q, IRANMANESH S, et al. Few-shot adversarial domain adaptation[C]//Advances in Neural Information Processing Systems, 2017:6670-6680.
[71]	FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//Proceedings of the 34th International Conference on Machine Learning, 2017:1126-1135.
[72]	RUSU A A, RAO D, SYGNOWSKI J, et al. Meta-learning with latent embedding optimization[EB/OL]. (2018-07-16)[2019-12-20]. https://arxiv.xilesou.top/pdf/1807.05960.
[73]	RAVI S, LAROCHELLE H. Optimization as a model for few-shot learning[C]//International Conference on Learning Representations (ICLR), 2017.
[74]	SANTORO A, BARTUNOV S, BOTVINICK M, et al. Meta-learning with memory-augmented neural networks[C]//International Conference on Machine Learning, 2016:1842-1850.
[75]	HILLIARD N, PHILLIPS L, HOWLAND S, et al. Few-shot learning with metric-agnostic conditional embeddings[EB/OL]. (2018-02-12)[2019-12-20]. https://arxiv.xilesou.top/pdf/1802.04376.
[76]	KINGMA D P, BA J. Adam:A method for stochastic optimization[EB/OL]. (2014-12-22)[2019-12-20]. https://arxiv.xilesou.top/pdf/1412.6980.
[77]	DAI J, HE K, SUN J. Instance-aware semantic segmentation via multi-task network cascades[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016:3150-3158.
[78]	WEI S E, RAMAKRISHNA V, KANADE T, et al. Convolutional pose machines[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016:4724-4732.
[79]	ANDERSON P, HE X, BUEHLER C. Bottom-up and top-down attention for image captioning and visual question answering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016:6077-6086.
[80]	刘芳, 王洪娟, 黄光伟, 等. 基于自适应深度网络的无人机目标跟踪算法[J]. 航空学报, 2019,40(3):322332. LIU F, WANG H J, HUANG G W, et al. UAV target tracking algorithm based on adaptive depth network[J]. Acta Aeronautica et Astronautica Sinica,2019,40(3):322332(in Chinese).
[81]	张菁, 何友, 彭应宁, 等. 基于神经网络和人工势场的协同博弈路径规划[J]. 航空学报, 2019,40(3):322493. ZHANG J, HE Y, PENG Y N, et al. Neural network and artificial potential field based cooperative and adversarially path planning[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(3):322493(in Chinese).
[82]	石叶楠, 郑国磊. 三种用于加工特征识别的神经网络方法综述[J]. 航空学报, 2019, 40(9):022840. SHI Y N, ZHENG G L. A review of three neural network methods for manufacturing feature recognition[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(9):022840(in Chinese).
[83]	王华夏, 程咏梅, 刘楠. 面向山地区域光照变化下的鲁棒景象匹配方法[J]. 航空学报, 2017,38(10):321101. WANG H X, CHENG Y M, LIU N. A robust scene matching method for mountainous region with illumination variation[J]. Acta Aeronautica et Astronautica Sinica,2017,38(10):321101(in Chinese).
[84]	LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft coco:Common objects in context[C]//European Conference on Computer Vision, 2014:740-755.
[85]	KUZNETSOVA A, ROM H, ALLDRIN N, et al. The open images dataset v4:Unified image classification, object detection, and visual relationship detection at scale[EB/OL]. (2018-11-02)[2019-12-20]. https://arxiv.xilesou.top/pdf/1811.00982.
[86]	REN S, HE K, GIRSHICK R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems, 2015:91-99.
[87]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:Unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016:779-788.
[88]	SUN Z, BEBIS G, MILLER R. On-road vehicle detection:A review[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2006(5):694-711.
[89]	DOLLÁR P, WOJEK C, SCHIELE B, et al. Pedestrian detection:A benchmark[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2009:304-311.
[90]	SCHWARTZ E, KARLINSKY L, SHTOK J, et al. RepMet:Representative-based metric learning for classification and one-shot object detection[EB/OL]. (2018-06-12)[2019-12-20]. https://arXiv.preprintarXiv:1806.04728.
[91]	CHEN H, WANG Y, WANG G, et al. A low-shot transfer detector for object detection[C]//Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
[92]	ZHANG T, ZHANG Y, SUN X, et al. Comparison network for one-shot conditional object detection[EB/OL]. (2019-4-4)[2019-12-20]. https://arxiv.xilesou.top/pdf/1904.02317.
[93]	HSIEH T I, LO Y C, CHEN H T, et al. One-shot object detection with co-attention and co-excitation[C]//Advances in Neural Information Processing Systems, 2019:2721-2730.
[94]	FAN Q, ZHUO W, TAI Y W. Few-shot object detection with attention-RPN and multi-relation detector[EB/OL] (2019-08-06)[2019-12-20]. https://arxiv.org/abs/1908.01998.
[95]	RAHMAN S, KHAN S, PORIKLI F. Zero-shot object detection:Learning to simultaneously recognize and localize novel concepts[C]//Asian Conference on Computer Vision, 2018:547-563.
[96]	ZHU P, WANG H, SALIGRAMA V. Zero shot detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 30(4):998-1010.
[97]	BANSAL A, SIKKA K, SHARMA G, et al. Zero-shot object detection[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018:384-400.
[98]	DEMIREL B, CINBIS R G, IKIZLER-CINBIS N. Zero-shot object detection by hybrid region embedding[EB/OL]. (2018-5-16)[2019-12-20]. https://arxiv.xilesou.top/pdf/1805.06157.
[99]	RAHMAN S, KHAN S, BARNES N. Polarity loss for zero-shot object detection[EB/OL]. (2018-11-22)[2019-12-20]. https://arxiv.xilesou.top/pdf/1811.08982.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

面向智能感知的小样本学习研究综述

A survey of few shot learning based on intelligent perception

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 99

相关文章 15

编辑推荐

Metrics

本文评价

[1]	罗旭东, 吴一全, 陈金林. 无人机航拍影像目标检测与语义分割的深度学习方法研究进展[J]. 航空学报, 2024, 45(6): 28822-028822.
[2]	甘纪强, 王小平. 基于虚拟样本生成的铺丝表面缺陷检测[J]. 航空学报, 2024, 45(1): 428624-428624.
[3]	陈立群, 邹旭, 张磊, 朱颖盼, 王港, 陈金勇. 基于国产商用器件的星载智能目标检测技术[J]. 航空学报, 2023, 44(S2): 728860-728860.
[4]	马金毅, 王灿, 薛涛, 艾剑良, 董一群. 空战格斗飞行机动数据库建立及应用[J]. 航空学报, 2023, 44(S1): 727538-727538.
[5]	奉志强, 谢志军, 包正伟, 陈科伟. 基于改进YOLOv5的无人机实时密集小目标检测算法[J]. 航空学报, 2023, 44(7): 327106-327106.
[6]	何磊, 钱炜祺, 董康生, 易贤, 柴聪聪. 基于卷积神经网络的结冰翼型气动特性建模[J]. 航空学报, 2023, 44(5): 126434-126434.
[7]	冒国韬, 邓天民, 于楠晶. 基于多尺度分割注意力的无人机航拍图像目标检测算法[J]. 航空学报, 2023, 44(5): 326738-326738.
[8]	陈唯实, 刘佳, 王青斌, 卢贤锋, 张洁, 陈小龙, 黄毅峰. 气象雷达探鸟技术综述[J]. 航空学报, 2023, 44(5): 26781-026781.
[9]	苑玉彬, 吴一全, 赵朗月, 陈金林, 赵其昌. 基于深度学习的无人机航拍视频多目标检测与跟踪研究进展[J]. 航空学报, 2023, 44(18): 28334-028334.
[10]	梁益铭, 李广宁, 徐敏. 基于机器学习的智能控制数值虚拟飞行方法[J]. 航空学报, 2023, 44(17): 128098-81280986.
[11]	陈小前, 赵勇, 霍森林, 张泽雨, 都柄晓. 多尺度结构拓扑优化设计方法综述[J]. 航空学报, 2023, 44(15): 528863-528863.
[12]	宋玉存, 葛泉波, 朱军龙, 陆振宇. 基于梯度差自适应学习率优化的改进YOLOX目标检测算法[J]. 航空学报, 2023, 44(14): 327951-327951.
[13]	李子豪, 王正平, 贺云涛. 基于自适应协同注意力机制的航拍密集小目标检测算法[J]. 航空学报, 2023, 44(13): 327944-327944.
[14]	王强, 吴乐天, 王勇, 王欢, 杨万扣. 基于关键点检测的红外弱小目标检测[J]. 航空学报, 2023, 44(10): 328173-328173.
[15]	段玉聪, 王学德, 周鑫, 张佩宇, 郭西洋, 成星, 樊军伟. 粉末床激光成形熔池辐射强度信号的机器学习[J]. 航空学报, 2022, 43(9): 425855-425855.