论文阅读目录

图像分类

Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.

Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.

Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2818-2826.

Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2017, 31(1).

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.

He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.

Lin M, Chen Q, Yan S. Network in network[J]. arXiv preprint arXiv:1312.4400, 2013.

目标检测

1. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.
2. He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904-1916.
3. Girshick R. Fast r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1440-1448.
4. 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 and machine intelligence, 2016, 39(6): 1137-1149.[faster_rcnn_pytorch]
5. Kong T, Yao A, Chen Y, et al. Hypernet: Towards accurate region proposal generation and joint object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 845-853.[pdf]
6. Cai Z, Fan Q, Feris R S, et al. A unified multi-scale deep convolutional neural network for fast object detection[C]//European conference on computer vision. Springer, Cham, 2016: 354-370.
7. Kim K H, Hong S, Roh B, et al. Pvanet: Deep but lightweight neural networks for real-time object detection[J]. arXiv preprint arXiv:1608.08021, 2016.
8. Li Z, Peng C, Yu G, et al. Light-head r-cnn: In defense of two-stage object detector[J]. arXiv preprint arXiv:1711.07264, 2017.[pdf]
9. Gidaris S, Komodakis N. Object detection via a multi-region and semantic segmentation-aware cnn model[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1134-1142.[pdf]
10. Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2117-2125.
11. Baek Y, Lee B, Han D, et al. Character region awareness for text detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 9365-9374.[pdf]
12. Dai J, Li Y, He K, et al. R-fcn: Object detection via region-based fully convolutional networks[J]. arXiv preprint arXiv:1605.06409, 2016.
13. Zhu Y, Zhao C, Wang J, et al. Couplenet: Coupling global structure with local parts for object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 4126-4134.[pdf]
14. He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2961-2969.
15. Cai Z, Vasconcelos N. Cascade r-cnn: Delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6154-6162.[pdf]
16. Sermanet P, Eigen D, Zhang X, et al. Overfeat: Integrated recognition, localization and detection using convolutional networks[J]. arXiv preprint arXiv:1312.6229, 2013.
17. 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. 2016: 779-788.[pdf] [pytorch_code]
18. Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271.[yolov2_pytorch]
19. Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.
20. Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: Optimal Speed and Accuracy of Object Detection[J]. arXiv preprint arXiv:2004.10934, 2020.
21. Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//European conference on computer vision. Springer, Cham, 2016: 21-37.
22. Tan M, Pang R, Le Q V. Efficientdet: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 10781-10790.
23. Howard A G, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv:1704.04861, 2017.
24. He Y, Zhu C, Wang J, et al. Bounding box regression with uncertainty for accurate object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 2888-2897.

 

小样本图像分类

 

小样本目标检测

综述：

1. 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(6).
2. Kang B , Liu Z , Wang X , et al. Few-Shot Object Detection via Feature Reweighting[J]. 2020.[code] [code_pytorch]
3. Wu X, Sahoo D, Hoi S. Meta-rcnn: Meta learning for few-shot object detection[C]//Proceedings of the 28th ACM International Conference on Multimedia. 2020: 1679-1687.
4. Fan Q, Zhuo W, Tang C K, et al. Few-shot object detection with attention-RPN and multi-relation detector[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 4013-4022.[作者code]
5. Fu K, Zhang T, Zhang Y, et al. Meta-SSD: Towards fast adaptation for few-shot object detection with meta-learning[J]. IEEE Access, 2019, 7: 77597-77606.
6. Chen H, Wang Y, Wang G, et al. Lstd: A low-shot transfer detector for object detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1).
7. Perez-Rua J M, Zhu X, Hospedales T M, et al. Incremental few-shot object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 13846-13855.
8. Karlinsky L, Shtok J, Harary S, et al. Repmet: Representative-based metric learning for classification and few-shot object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 5197-5206.
9. Yang Z, Wang Y, Chen X, et al. Context-transformer: tackling object confusion for few-shot detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(07): 12653-12660.
10. Dong X, Zheng L, Ma F, et al. Few-example object detection with model communication[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 41(7): 1641-1654.
11. Wang T, Zhang X, Yuan L, et al. Few-shot adaptive faster r-cnn[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 7173-7182.
12. Wang X, Huang T E, Darrell T, et al. Frustratingly simple few-shot object detection[J]. arXiv preprint arXiv:2003.06957, 2020.[code]
13. Liu L, Ma B, Zhang Y, et al. AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection[J]. arXiv preprint arXiv:2011.14667, 2020.
14. Wu J, Liu S, Huang D, et al. Multi-Scale Positive Sample Refinement for Few-Shot Object Detection[C]//European Conference on Computer Vision. Springer, Cham, 2020: 456-472.
15. Xiao Y, Marlet R. Few-shot object detection and viewpoint estimation for objects in the wild[C]//European Conference on Computer Vision. Springer, Cham, 2020: 192-210.
16. Zhang S, Luo D, Wang L, et al. Few-shot object detection by second-order pooling[C]//Proceedings of the Asian Conference on Computer Vision. 2020.
17. Yang Y, Wei F, Shi M, et al. Restoring negative information in few-shot object detection[J]. arXiv preprint arXiv:2010.11714, 2020.
18. Yang Z, Wang Y, Chen X, et al. Context-transformer: tackling object confusion for few-shot detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(07): 12653-12660.
19. Zhang G, Cui K, Wu R, et al. PNPDet: Efficient Few-Shot Detection Without Forgetting via Plug-and-Play Sub-Networks[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 3823-3832.
20. Xu H, Wang X, Shao F, et al. Few-Shot Object Detection via Sample Processing[J]. IEEE Access, 2021, 9: 29207-29221.
21. Li Y, Feng W, Lyu S, et al. MM-FSOD: Meta and metric integrated few-shot object detection[J]. arXiv preprint arXiv:2012.15159, 2020.
22. Kim G, Jung H G, Lee S W. Few-Shot Object Detection via Knowledge Transfer[C]//2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020: 3564-3569.
23. Sun B, Li B, Cai S, et al. FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding[J]. arXiv preprint arXiv:2103.05950, 2021.[code]
24. Zhang G, Luo Z, Cui K, et al. Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning[J]. arXiv preprint arXiv:2103.11731, 2021.
25. Li X, Deng J, Fang Y. Few-Shot Object Detection on Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021.
26. Zhang W, Wang Y X, Forsyth D A. Cooperating RPN's Improve Few-Shot Object Detection[J]. arXiv preprint arXiv:2011.10142, 2020.
27. Wu A, Han Y, Zhu L, et al. Universal-Prototype Augmentation for Few-Shot Object Detection[J]. arXiv preprint arXiv:2103.01077, 2021.
28. Chen X, Jiang M, Zhao Q. Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection[J]. arXiv preprint arXiv:2007.12104, 2020.
29. Han G, Huang S, Ma J, et al. Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment[J]. arXiv preprint arXiv:2104.07719, 2021.
30. Li B, Yang B, Liu C, et al. Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection[J]. arXiv preprint arXiv:2103.04612, 2021.
31. Chen T I, Liu Y C, Su H T, et al. Should I Look at the Head or the Tail? Dual-awareness Attention for Few-Shot Object Detection[J]. arXiv preprint arXiv:2102.12152, 2021.
32. Xiao Z, Xue W, Zhong P. Few-shot Object Detection with Self-adaptive Attention Network for Remote Sensing Images[J]. arXiv preprint arXiv:2009.12596, 2020.
33. Zhang G, Cui K, Wu R, et al. PNPDet: Efficient Few-Shot Detection Without Forgetting via Plug-and-Play Sub-Networks[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 3823-3832.

小样本图像生成

1. Yan Hong, Li Niu, Jianfu Zhang, Weijie Zhao, Chen Fu, Liqing Zhang: "F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation." ACM MM (2020).[pdf] [code] [论文阅读笔记]
2.  

 

 

旋转目标检测

 

姿态检测

 

ROC文字识别