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Few-shot segmentation segments object regions of new classes with a few of manual annotations.
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Hu, T., Yang, P., Zhang, C., Yu, G., Mu, Y., Snoek, C. G. (2019). Attention-based Multi-Context Guiding for Few-Shot Semantic Segmentation.In Proceedings of the Association for the Advance of Artificial Intelligence
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Caelles, S., Maninis, K. K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Van Gool, L. (2017). One-shot video object segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 221-230)
2017
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Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848
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2019
Closest in time.
Zhang, C., Lin, G., Liu, F., Yao, R., Shen, C. (2019). CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5217-5226)
2019
Closest in time.