Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9

1979

Earlier work this paper cites.

Fink, M.: Object classification from a single example utilizing class relevance metrics. Advances in Neural Information Processing Systems 17

2005

Earlier work this paper cites.

Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence 28

2006

Earlier work this paper cites.

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255. Ieee (2009)

2009

Earlier work this paper cites.

Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. International Journal of Computer Vision 88

2010

Earlier work this paper cites.

Lake, B., Salakhutdinov, R., Gross, J., Tenenbaum, J.: One shot learning of simple visual concepts. In: Proceedings of the Annual Meeting of the Cognitive Science Society. vol. 33, pp. 2568–2573 (2011)

2011

Earlier work this paper cites.

Kulis, B., et al.: Metric learning: A survey. Foundations and Trends® in Machine Learning 5

2013

Earlier work this paper cites.

Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Proceedings of the European Conference on Computer Vision. pp. 297–312. Springer (2014)

2014

Earlier work this paper cites.

Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: Common objects in context. In: Proceedings of the European Conference on Computer Vision. pp. 740–755. Springer (2014)

2014

Earlier work this paper cites.

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

Original

2014

Earlier work this paper cites.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3431–3440 (2015)

2015

Earlier work this paper cites.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 234–241. Springer (2015)

2015

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)

2016

Earlier work this paper cites.

Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. Advances in Neural Information Processing Systems 29

2016

Earlier work this paper cites.

Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40

2017

Earlier work this paper cites.

Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2117–2125 (2017)

2017

Earlier work this paper cites.

Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: International Conference on Learning Representations (2017)

2017

Earlier work this paper cites.

Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation. In: Proceedings of the British Machine Vision Conference. pp. 167.1–167.13 (2017)

2017

Earlier work this paper cites.

Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems. pp. 4080–4090 (2017)

2017

Cited alongside, same era.

Triantafillou, E., Zemel, R., Urtasun, R.: Few-shot learning through an information retrieval lens. In: Advances in Neural Information Processing Systems. pp. 2252–2262 (2017)

2017

Cited alongside, same era.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems. pp. 5998–6008 (2017)

2017

Cited alongside, same era.

Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2881–2890 (2017)

2017

Cited alongside, same era.

Yang, B., Liu, C., Li, B., Jiao, J., Ye, Q.: Prototype mixture models for few-shot semantic segmentation. In: Proceedings of the European Conference on Computer Vision. pp. 763–778. Springer (2020)

2020

Later among the works it cites.

Zhang, X., Wei, Y., Yang, Y., Huang, T.S.: SG-One: Similarity guidance network for one-shot semantic segmentation. IEEE Transactions on Cybernetics 50

2020

Later among the works it cites.

Azad, R., Fayjie, A.R., Kauffmann, C., Ben Ayed, I., Pedersoli, M., Dolz, J.: On the texture bias for few-shot CNN segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 2674–2683 (2021)

2021

Later among the works it cites.

Boudiaf, M., Kervadec, H., Masud, Z.I., Piantanida, P., Ben Ayed, I., Dolz, J.: Few-shot segmentation without meta-learning: A good transductive inference is all you need? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13979–13988 (2021)

2021