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While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems.
G.-J. Qi, X.-S. Hua, Y. Rui, T. Mei, J. Tang, and H.-J. Zhang, “Concurrent multiple instance learning for image categorization,” in 2007 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
P. Dollár, B. Babenko, S. Belongie, P. Perona, and Z. Tu, “Multiple component learning for object detection,” in European conference on computer vision . Springer, 2008, pp. 211–224
2008
Earlier work this paper cites.
B. Babenko, M.-H. Yang, and S. Belongie, “Robust object tracking with online multiple instance learning,” IEEE transactions on pattern analysis and machine intelligence , vol. 33, no. 8, pp. 1619–1632, 2010
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International journal of computer vision , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
2014
Earlier work this paper cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 580–587
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in ICML deep learning workshop , vol. 2. Lille, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. Zhang, D. Meng, C. Li, L. Jiang, Q. Zhao, and J. Han, “A self-paced multiple-instance learning framework for co-saliency detection,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 594–602
2015
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
Earlier work this paper cites.
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr, “Fully-convolutional siamese networks for object tracking,” in European conference on computer vision . Springer, 2016, pp. 850–865
2016
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” 2016
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” in Advances in neural information processing systems , 2016, pp. 3630–3638
2016
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in Advances in neural information processing systems , 2017, pp. 4077–4087
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
J. Han, G. Cheng, Z. Li, and D. Zhang, “A unified metric learning-based framework for co-saliency detection,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 28, no. 10, pp. 2473–2483, 2017
2017
Cited alongside, same era.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
Cited alongside, same era.
J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, “Dual attention network for scene segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 3146–3154
2019
Later among the works it cites.
Y. Cao, J. Xu, S. Lin, F. Wei, and H. Hu, “Gcnet: Non-local networks meet squeeze-excitation networks and beyond,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
Later among the works it cites.
X. Zhu, D. Cheng, Z. Zhang, S. Lin, and J. Dai, “An empirical study of spatial attention mechanisms in deep networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 6688–6697
2019
Later among the works it cites.
Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” in Proceedings of the IEEE international conference on computer vision , 2019, pp. 9627–9636
2019
Later among the works it cites.
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A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales, “Learning to compare: Relation network for few-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1199–1208
2018
Cited alongside, same era.
2018
Cited alongside, same era.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7794–7803
2018
Cited alongside, same era.
H. Zhao, Y. Zhang, S. Liu, J. Shi, C. Change Loy, D. Lin, and J. Jia, “Psanet: Point-wise spatial attention network for scene parsing,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 267–283
2018
Cited alongside, same era.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Cited alongside, same era.
H. Law and J. Deng, “Cornernet: Detecting objects as paired keypoints,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 734–750
2018
Cited alongside, same era.
2018
Cited alongside, same era.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” arXiv preprint arXiv:1904.07850 , 2019
2019
Later among the works it cites.
Q. Fan, W. Zhuo, C.-K. Tang, and Y.-W. Tai, “Few-shot object detection with attention-rpn and multi-relation detector,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4013–4022
2020
Later among the works it cites.
J.-M. Perez-Rua, X. Zhu, T. M. Hospedales, and T. Xiang, “Incremental few-shot object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 13 846–13 855
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Fan, J.-G. Yu, Z. Liang, J. Ou, C. Gao, G.-S. Xia, and Y. Li, “Fgn: Fully guided network for few-shot instance segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9172–9181
2020
Later among the works it cites.
W. Liu, C. Zhang, G. Lin, and F. Liu, “Crnet: Cross-reference networks for few-shot segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4165–4173
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Emami, M. M. Aliabadi, M. Dong, and R. B. Chinnam, “Spa-gan: Spatial attention gan for image-to-image translation,” IEEE Transactions on Multimedia , vol. 23, pp. 391–401, 2020
2020
Later among the works it cites.
J. Li, X. Liu, W. Zhang, M. Zhang, J. Song, and N. Sebe, “Spatio-temporal attention networks for action recognition and detection,” IEEE Transactions on Multimedia , vol. 22, no. 11, pp. 2990–3001, 2020
2020
Later among the works it cites.
Q. Fan, D.-P. Fan, H. Fu, C.-K. Tang, L. Shao, and Y.-W. Tai, “Group collaborative learning for co-salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 288–12 298
2021
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