Fetching the paper…
Reading the bibliography…
Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations.
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.
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: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3213–3223 (2016)
2016
Earlier work this paper cites.
Gong, K., Liang, X., Zhang, D., Shen, X., Lin, L.: Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 932–940 (2017)
2017
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 2961–2969 (2017)
2017
Earlier work this paper cites.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp. 618–626 (2017)
2017
Earlier work this paper cites.
Kanezaki, A.: Unsupervised image segmentation by backpropagation. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). pp. 1543–1547. IEEE (2018)
2018
Earlier work this paper cites.
Bielski, A., Favaro, P.: Emergence of object segmentation in perturbed generative models. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. pp. 7256–7266 (2019)
2019
Earlier work this paper cites.
Chen, M., Artières, T., Denoyer, L.: Unsupervised object segmentation by redrawing. In: Advances in Neural Information Processing Systems 32 (NIPS 2019). pp. 12705–12716. Curran Associates, Inc. (2019)
2019
Earlier work this paper cites.
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/CVF International Conference on Computer Vision. pp. 7334–7344 (2019)
2019
Earlier work this paper cites.
Ji, X., Henriques, J.F., Vedaldi, A.: Invariant information clustering for unsupervised image classification and segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9865–9874 (2019)
2019
Earlier work this paper cites.
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
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
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Pires, B., Guo, Z., Azar, M., et al.: Bootstrap your own latent: A new approach to self-supervised learning. In: Neural Information Processing Systems (2020)
2020
Cited alongside, same era.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9729–9738 (2020)
2020
Cited alongside, same era.
Kim, W., Kanezaki, A., Tanaka, M.: Unsupervised learning of image segmentation based on differentiable feature clustering. IEEE Transactions on Image Processing 29
2020
Kim, D., Hong, B.W.: Unsupervised segmentation incorporating shape prior via generative adversarial networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7324–7334 (2021)
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Mirsadeghi, S.E., Royat, A., Rezatofighi, H.: Unsupervised image segmentation by mutual information maximization and adversarial regularization. IEEE Robotics and Automation Letters 6
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Liu, Y., Guo, H.: Peer loss functions: Learning from noisy labels without knowing noise rates. In: International Conference on Machine Learning. pp. 6226–6236. PMLR (2020)
2020
Cited alongside, same era.
Ouali, Y., Hudelot, C., Tami, M.: Autoregressive unsupervised image segmentation. In: European Conference on Computer Vision. pp. 142–158. Springer (2020)
2020
Cited alongside, same era.
Pinheiro, P.O., Almahairi, A., Benmalek, R.Y., Golemo, F., Courville, A.C.: Unsupervised learning of dense visual representations. In: NeurIPS (2020)
2020
Cited alongside, same era.
2021
Cited alongside, same era.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the International Conference on Computer Vision (ICCV) (2021)
2021
Cited alongside, same era.
Chefer, H., Gur, S., Wolf, L.: Transformer interpretability beyond attention visualization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 782–791 (2021)
2021
Cited alongside, same era.
Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15750–15758 (2021)
2021
Cited alongside, same era.
Cho, J.H., Mall, U., Bala, K., Hariharan, B.: Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16794–16804 (2021)
2021
Cited alongside, same era.
Shi, X., Khademi, S., Li, Y., van Gemert, J.: Zoom-cam: Generating fine-grained pixel annotations from image labels. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 10289–10296. IEEE (2021)
2021
Closest in time.
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: Transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)
2021
Closest in time.
Van Gansbeke, W., Vandenhende, S., Georgoulis, S., Van Gool, L.: Unsupervised semantic segmentation by contrasting object mask proposals. In: International Conference on Computer Vision (2021)
2021
Closest in time.
Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: Dense contrastive learning for self-supervised visual pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3024–3033 (2021)
2021
Closest in time.
2021
Closest in time.
Xie, Z., Lin, Y., Zhang, Z., Cao, Y., Lin, S., Hu, H.: Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16684–16693 (2021)
2021
Closest in time.
2021
Closest in time.
Yao, Z., Cao, Y., Lin, Y., Liu, Z., Zhang, Z., Hu, H.: Leveraging batch normalization for vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 413–422 (2021)
2021
Closest in time.
Zou, Y., Zhang, Z., Zhang, H., Li, C.L., Bian, X., Huang, J.B., Pfister, T.: Pseudoseg: Designing pseudo labels for semantic segmentation. International Conference on Learning Representations (ICLR) (2021)
2021
Closest in time.