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In this work, we address the challenging task of few-shot segmentation.
Adaptive masked weight imprinting for few-shot segmentation
Siam, M., Oreshkin, B., 2019 · 1902
Earlier work this paper cites.
Improved baselines with momentum contrastive learning
Chen, X., Fan, H., Girshick, R., He, K., 2020b · 2003
Earlier work this paper cites.
Efficient graph-based image segmentation
Felzenszwalb, P.F., Huttenlocher, D.P., 2004 · 2004
Earlier work this paper cites.
Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.A., Guo, Z.D., Azar, M.G., et al., 2020 · 2006
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping, in: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), IEEE. pp. 1735–1742
Hadsell, R., Chopra, S., LeCun, Y., 2006 · 2006
Earlier work this paper cites.
Salient region detection and segmentation, in: Computer Vision Systems: 6th International Conference, ICVS 2008 Santorini, Greece, May 12-15, 2008 Proceedings 6, Springer. pp. 66–75
Achanta, R., Estrada, F., Wils, P., Süsstrunk, S., 2008 · 2008
Earlier work this paper cites.
Prototype mixture models for few-shot semantic segmentation
Yang, B., Liu, C., Li, B., Jiao, J., Ye, Q., 2020 · 2008
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A., 2010 · 2010
Earlier work this paper cites.
Slic superpixels compared to state-of-the-art superpixel methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S., 2012 · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context, in: ECCV, pp. 740–755
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014 · 2014
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440
Long, J., Shelhamer, E., Darrell, T., 2015 · 2015
Earlier work this paper cites.
One-shot learning for semantic segmentation
Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B., 2017 · 2017
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L., 2018 · 2018
Earlier work this paper cites.
Few-shot semantic segmentation with prototype learning, in: British Machine Vision Conference
Dong, N., Xing, E., 2018 · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Oord, A.v.d., Li, Y., Vinyals, O., 2018 · 2018
Cited alongside, same era.
Conditional networks for few-shot semantic segmentation, in: ICLR Workshop
Rakelly, K., Shelhamer, E., Darrell, T., Efros, A., Levine, S., 2018 · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733–3742
Wu, Z., Xiong, Y., Yu, S.X., Lin, D., 2018 · 2018
Cited alongside, same era.
Multi-scale discriminative location-aware network for few-shot semantic segmentation, in: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), IEEE. pp. 42–47
Dong, Z., Zhang, R., Shao, X., Zhou, H., 2019 · 2019
Cited alongside, same era.
Invariant information clustering for unsupervised image classification and segmentation, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 9865–9874
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
Boudiaf, M., Kervadec, H., Ziko, I.M., Piantanida, P., Ayed, I.B., Dolz, J., 2021 · 2021
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Abpnet: Adaptive background modeling for generalized few shot segmentation, in: ACM MM, pp. 2271–2280
Dong, K., Yang, W., Xu, Z., Huang, L., Yu, Z., 2021 · 2021
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Region-aware contrastive learning for semantic segmentation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16291–16301
Hu, H., Cui, J., Wang, L., 2021 · 2021
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Simpler is better: Few-shot semantic segmentation with classifier weight transformer, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 8721–8730
Lu, Z., He, S., Zhu, X., Zhang, L., Song, Y.Z., Xiang, T., 2021 · 2021
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Ji, X., Henriques, J.F., Vedaldi, A., 2019 · 2019
Cited alongside, same era.
Feature weighting and boosting for few-shot segmentation, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 622–631
Nguyen, K., Todorovic, S., 2019 · 2019
Cited alongside, same era.
Amp: Adaptive masked proxies for few-shot segmentation, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 5249–5258
Siam, M., Oreshkin, B.N., Jagersand, M., 2019 · 2019
Cited alongside, same era.
Panet: Few-shot image semantic segmentation with prototype alignment, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 9197–9206
Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J., 2019 · 2019
Cited alongside, same era.
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
Zhang, C., Lin, G., Liu, F., Yao, R., Shen, C., 2019 · 2019
Cited alongside, same era.
Contrastive learning of global and local features for medical image segmentation with limited annotations
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E., 2020 · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R., 2020 · 2020
Cited alongside, same era.
Fss-1000: A 1000-class dataset for few-shot segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2869–2878
Li, X., Wei, T., Chen, Y.P., Tai, Y.W., Tang, C.K., 2020 · 2020
Cited alongside, same era.
Detco: Unsupervised contrastive learning for object detection, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8392–8401
Xie, E., Ding, J., Wang, W., Zhan, X., Xu, H., Sun, P., Li, Z., Luo, P., 2021 · 2021
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Self-guided and cross-guided learning for few-shot segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6933–6942
Zhang, B., Xiao, J., Qin, T., 2021 · 2021
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MSI: Maximize support-set information for few-shot segmentation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 19266–19276
Moon, S., Sohn, S.S., Zhou, H., Yoon, S., Pavlovic, V., Khan, M.H., Kapadia, M., 2023 · 2023
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Hierarchical dense correlation distillation for few-shot segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 23641–23651
Peng, B., Tian, Z., Wu, X., Wang, C., Liu, S., Su, J., Jia, J., 2023 · 2023
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Self-calibrated cross attention network for few-shot segmentation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 655–665
Xu, Q., Zhao, W., Lin, G., Long, C., 2023 · 2023
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MIANet: Aggregating unbiased instance and general information for few-shot semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7131–7140
Yang, Y., Chen, Q., Feng, Y., Huang, T., 2023 · 2023
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Visual prompting for generalized few-shot segmentation: A multi-scale approach, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 23470–23480
Hossain, M.R.I., Siam, M., Sigal, L., Little, J.J., 2024 · 2024
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Eliminating feature ambiguity for few-shot segmentation, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 416–433
Xu, Q., Lin, G., Loy, C.C., Long, C., Li, Z., Zhao, R., 2024 · 2024
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LLaFS: When large language models meet few-shot segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3065–3075
Zhu, L., Chen, T., Ji, D., Ye, J., Liu, J., 2024 · 2024
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Physically-guided open vocabulary segmentation with weighted patched alignment loss
Liu, W., Lou, J., Wang, X., Zhou, W., Cheng, J., Yang, X., 2025 · 2025
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