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This work considers supervised contrastive learning for semantic segmentation.
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research
2008
Earlier work this paper cites.
2010
Earlier work this paper cites.
Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., Yuille, A.: The role of context for object detection and semantic segmentation in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs (2016)
2016
Earlier work this paper cites.
2016
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.
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence
2016
Earlier work this paper cites.
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
2016
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017)
2017
Earlier work this paper cites.
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Earlier work this paper cites.
Berman, M., Triki, A.R., Blaschko, M.B.: The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Earlier work this paper cites.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. pp. 833–851. Springer International Publishing, Cham (2018)
2018
Earlier work this paper cites.
Ke, T.W., Hwang, J.J., Liu, Z., Yu, S.X.: Adaptive affinity fields for semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV) (September 2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Earlier work this paper cites.
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. pp. 432–448. Springer International Publishing, Cham (2018)
2018
Earlier work this paper cites.
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision
2018
Cited alongside, same era.
Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., Bengio, Y.: Learning deep representations by mutual information estimation and maximization. In: ICLR (2019)
2019
Cited alongside, same era.
Alonso, I., Sabater, A., Ferstl, D., Montesano, L., Murillo, A.C.: Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank. In: Proceedings of the IEEE International Conference on Computer Vision (2021)
2021
Later among the works it cites.
Hénaff, O.J., Koppula, S., Alayrac, J.B., Oord, A.v.d., Vinyals, O., Carreira, J.: Efficient Visual Pretraining with Contrastive Detection. International Conference on Computer Vision (2021)
2021
Later among the works it cites.
Hu, H., Cui, J., Wang, L.: Region-aware contrastive learning for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 16291–16301 (October 2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 10012–10022 (October 2021)
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Hwang, J.J., Yu, S.X., Shi, J., Collins, M.D., Yang, T.J., Zhang, X., Chen, L.C.: Segsort: Segmentation by discriminative sorting of segments. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 7334–7344 (2019)
2019
Cited alongside, same era.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)
2019
Cited alongside, same era.
Saunshi, N., Plevrakis, O., Arora, S., Khodak, M., Khandeparkar, H.: A theoretical analysis of contrastive unsupervised representation learning. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 5628–5637. PMLR (09–15 Jun 2019),
2019
Cited alongside, same era.
Takikawa, T., Acuna, D., Jampani, V., Fidler, S.: Gated-scnn: Gated shape cnns for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (October 2019)
2019
Cited alongside, same era.
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., Liu, W., Xiao, B.: Deep high-resolution representation learning for visual recognition. TPAMI (2019)
2019
Cited alongside, same era.
Zhao, S., Wang, Y., Yang, Z., Cai, D.: Region mutual information loss for semantic segmentation. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019),
2019
Cited alongside, same era.
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. Advances in Neural Information Processing Systems
2020
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning. pp. 1597–1607. PMLR (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
Pissas*, T., Ravasio*, C.S., Da Cruz, L., Bergeles, C.: Effective semantic segmentation in cataract surgery: What matters most? In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. pp. 509–518. Springer International Publishing, Cham (2021)
2021
Later among the works it cites.
Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 12179–12188 (October 2021)
2021
Later among the works it cites.
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: Transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7262–7272 (October 2021)
2021
Later among the works it cites.
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: Transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7262–7272 (October 2021)
2021
Later among the works it cites.
Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., Van Gool, L.: Exploring cross-image pixel contrast for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7303–7313 (October 2021)
2021
Later among the works it cites.
Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: Dense contrastive learning for self-supervised visual pre-training. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Later among the works it cites.
Xiao, T., Wang, X., Efros, A.A., Darrell, T.: What should not be contrastive in contrastive learning. In: International Conference on Learning Representations (2021),
2021
Later among the works it cites.
Xie, E., Ding, J., Wang, W., Zhan, X., Xu, H., Sun, P., Li, Z., Luo, P.: Detco: Unsupervised contrastive learning for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 8392–8401 (October 2021)
2021
Later among the works it cites.
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems (2021),
2021
Later among the works it cites.
Zhang, Y., He, R., Liu, Z., Lim, K.H., Bing, L.: An unsupervised sentence embedding method by mutual information maximization. EMNLP (2021)
2021
Later among the works it cites.
Zhao, X., Vemulapalli, R., Mansfield, P.A., Gong, B., Green, B., Shapira, L., Wu, Y.: Contrastive learning for label efficient semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 10623–10633 (October 2021)
2021
Later among the works it cites.
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., Zhang, L.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6881–6890 (June 2021)
2021
Later among the works it cites.