1 |
SHEN D G, WU G R, SUK H I. Deep learning in medical image analysis[J]. Annual Review of Biomedical Engineering, 2017, 19: 221-248. 10.1146/annurev-bioeng-071516-044442
|
2 |
黄元涛. 基于深度学习的藏羚羊检测与跟踪[D]. 西安:西安电子科技大学, 2020: 3-69.
|
|
HUANG Y T. Detection and tracking of Tibetan Antelope based on deep learning[D]. Xi’an: Xidian University, 2020: 3-69.
|
3 |
BANSAL A, SIKKA K, SHARMA G, et al. Zero-shot object detection[C]// Proceedings of the 2018 European Conference on Computer Vision, LNCS 11205. Cham: Springer, 2018: 397-414.
|
4 |
RAHMAN, S, KHAN, S, PORIKLI F. Zero-shot object detection: learning to simultaneously recognize and localize novel concepts[C]// Proceedings of the 2018 Asian Conference on Computer Vision, LNCS 11361. Cham: Springer, 2019: 547-563.
|
5 |
ZHU P K, WANG H X, SALIGRAMA V. Zero-shot detection[J]. IEEE Transactions on Circuits and System for Video Technology, 2020, 30(4): 998-1010. 10.1109/tcsvt.2019.2899569
|
6 |
潘兴甲,张旭龙,董未名,等. 小样本目标检测的研究现状[J]. 南京信息工程大学学报(自然科学版), 2019, 11(6): 698-705.
|
|
PAN X J, ZHANG X L, DONG W M, et al. A survey of few-shot object detection[J]. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 2019, 11(6): 698-705.
|
7 |
VINYALS O, BLUNDELL C, LILLICRAP T, et al. Matching networks for one shot learning[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2016: 3637-3645.
|
8 |
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. New York: JMLR.org, 2017: 1126-1135. 10.1109/icra.2016.7487173
|
9 |
SNELL J, SWERSKY K, ZEMEL R S. Prototypical networks for few-shot learning [C]// Proceedings of the 31st Annual Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 178-181.
|
10 |
MISRA I, SHRIVASTAVA A, HEBERT M. Watch and learn: semi-supervised learning of object detectors from videos[C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2015: 3593-3602. 10.1109/cvpr.2015.7298982
|
11 |
XING C, ROSTAMZADEH N, ORESHKIN B O. Adaptive cross-modal few-shot learning[C/OL]// Proceedings of the 33rd Conference on Neural Information Processing Systems. [2021-02-27]..
|
12 |
NI J, ZHANG S H, XIE H Y. Dual adversarial semantics-consistent network for generalized zero-shot learning[C/OL]// Proceedings of the 33rd Conference on Neural Information Processing Systems. [2021-02-27]..
|
13 |
REN M Y, LIAO R J, FETAYA E, et al. Incremental few-shot learning with attention attractor networks[C/OL]// Proceedings of the 33rd Conference on Neural Information Processing Systems. [2021-02-27]..
|
14 |
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, 2017, 39(6): 1137-1149. 10.1109/tpami.2016.2577031
|
15 |
FAN Q, ZHUO W, TANG C K, et al. Few-shot object detection with attention-RPN and multi-relation detector[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 4012-4021. 10.1109/cvpr42600.2020.00407
|
16 |
徐诚极,王晓峰,杨亚东. Attention-YOLO:引入注意力机制的YOLO检测算法[J]. 计算机工程与应用, 2019, 55(6): 13-23, 125.
|
|
XU C J, WANG X F, YANG Y D. Attention-YOLO: YOLO detection algorithm that introduces attention mechanism[J]. Computer Engineering and Applications, 2019, 55(6): 13-23, 125.
|
17 |
REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08) [2021-03-15]..
|
18 |
CHEN H, WANG Y L, WANG G Y, et al. LSTD: a low-shot transfer detector for object detection[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 2836-2843. 10.1609/aaai.v32i1.11716
|
19 |
KANG B Y, LIU Z, WANG X, et al. Few-shot object detection via feature reweighting[C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 8419-8428. 10.1109/iccv.2019.00851
|
20 |
REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 6517-6525. 10.1109/cvpr.2017.690
|
21 |
WOO S, PRAK J, LEE J Y, et al. CBAM: convolutional block attention module[C]// Proceedings of the 2018 European Conference on Computer Vision, LNCS 11211. Cham: Springer, 2018: 3-19.
|
22 |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7132-7141. 10.1109/cvpr.2018.00745
|
23 |
SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 618-626. 10.1109/iccv.2017.74
|
24 |
EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The PASCAL Visual Object Classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338. 10.1007/s11263-009-0275-4
|
25 |
EVERINGHAM M, ESLAMI S M A, VAN GOOL L, et al. The PASCAL Visual Object Classes challenge: a retrospective[J]. International Journal of Computer Vision, 2015, 111(1): 98-136. 10.1007/s11263-014-0733-5
|
26 |
MAATEN L V D, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579-2605.
|