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Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source domain (e.g.
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Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. In: ICLR. pp. 834–848 (2015)
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Dai, J., He, K., Sun, J.: Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: ICCV. pp. 1635–1643 (2015)
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Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR. pp. 3431–3440 (2015)
2015
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Int. Conf. Medical Image Computing and Computer-assisted Intervention. pp. 234–241 (2015)
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Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: Scale-aware semantic image segmentation. In: CVPR. pp. 3640–3649 (2016)
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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: CVPR. pp. 3213–3223 (2016), https://www.cityscapes-dataset.com/ , dataset license: https://www.cityscapes-dataset.com/license/
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Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. JMLR 17
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770–778 (2016)
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Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: Ground truth from computer games. In: ECCV. pp. 102–118 (2016), https://download.visinf.tu-darmstadt.de/data/from_games/
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Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In: CVPR. pp. 3234–3243 (2016), http://synthia-dataset.net/ , dataset license: CC BY-NC-SA 3.0
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Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: NeurIPS (2016)
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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. PAMI 40
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Souly, N., Spampinato, C., Shah, M.: Semi supervised semantic segmentation using generative adversarial network. In: ICCV. pp. 5688–5696 (2017)
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Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS. pp. 1195–1204 (2017)
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Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR. pp. 2881–2890 (2017)
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Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: CVPR. pp. 1851–1858 (2017)
2017
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Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV. pp. 801–818 (2018)
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Dai, D., Van Gool, L.: Dark model adaptation: Semantic image segmentation from daytime to nighttime. In: ITSC. pp. 3819–3824 (2018)
2018
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Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., Efros, A., Darrell, T.: Cycada: Cycle-consistent adversarial domain adaptation. In: ICML. pp. 1989–1998 (2018)
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Lee, K.H., Ros, G., Li, J., Gaidon, A.: Spigan: Privileged adversarial learning from simulation. In: ICLR (2018)
2018
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Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2018)
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Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: CVPR. pp. 7472–7481 (2018)
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Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR. pp. 7794–7803 (2018)
2018
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Yang, S., Peng, G.: Attention to refine through multi scales for semantic segmentation. In: Pacific Rim Conference on Multimedia. pp. 232–241 (2018)
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Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., Agrawal, A.: Context encoding for semantic segmentation. In: CVPR. pp. 7151–7160 (2018)
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Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C.C., Lin, D., Jia, J.: Psanet: Point-wise spatial attention network for scene parsing. In: ECCV. pp. 267–283 (2018)
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Zou, Y., Yu, Z., Kumar, B., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: ECCV. pp. 289–305 (2018)
2018
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Chen, M., Xue, H., Cai, D.: Domain adaptation for semantic segmentation with maximum squares loss. In: ICCV. pp. 2090–2099 (2019)
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Chen, Y., Li, W., Chen, X., Gool, L.V.: Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach. In: CVPR. pp. 1841–1850 (2019)
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Choi, J., Kim, T., Kim, C.: Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation. In: ICCV. pp. 6830–6840 (2019)
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Ding, H., Jiang, X., Liu, A.Q., Thalmann, N.M., Wang, G.: Boundary-aware feature propagation for scene segmentation. In: ICCV. pp. 6819–6829 (2019)
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Ding, X., Guo, Y., Ding, G., Han, J.: Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks. In: ICCV. pp. 1911–1920 (2019)
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Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: CVPR. pp. 3146–3154 (2019)
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Hoyer, L., Munoz, M., Katiyar, P., Khoreva, A., Fischer, V.: Grid saliency for context explanations of semantic segmentation. In: NeurIPS. pp. 6462–6473 (2019)
2019
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Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: Ccnet: Criss-cross attention for semantic segmentation. In: ICCV. pp. 603–612 (2019)
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Zheng, Z., Yang, Y.: Unsupervised scene adaptation with memory regularization in vivo. In: IJCAI. pp. 1076–1082 (2020)
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Araslanov, N., Roth, S.: Self-supervised augmentation consistency for adapting semantic segmentation. In: CVPR. pp. 15384–15394 (2021)
2021
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2019
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Li, Y., Yuan, L., Vasconcelos, N.: Bidirectional learning for domain adaptation of semantic segmentation. In: CVPR. pp. 6936–6945 (2019)
2019
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Lin, D., Shen, D., Shen, S., Ji, Y., Lischinski, D., Cohen-Or, D., Huang, H.: Zigzagnet: Fusing top-down and bottom-up context for object segmentation. In: CVPR. pp. 7490–7499 (2019)
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Luo, Y., Zheng, L., Guan, T., Yu, J., Yang, Y.: Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation. In: CVPR. pp. 2507–2516 (2019)
2019
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Tsai, Y.H., Sohn, K., Schulter, S., Chandraker, M.: Domain adaptation for structured output via discriminative patch representations. In: ICCV. pp. 1456–1465 (2019)
2019
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Vu, T.H., Jain, H., Bucher, M., Cord, M., Pérez, P.: Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation. In: CVPR. pp. 2517–2526 (2019)
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Vu, T.H., Jain, H., Bucher, M., Cord, M., Pérez, P.: Dada: Depth-aware domain adaptation in semantic segmentation. In: ICCV. pp. 7364–7373 (2019)
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Zhang, Q., Zhang, J., Liu, W., Tao, D.: Category anchor-guided unsupervised domain adaptation for semantic segmentation. In: NeurIPS. pp. 435–445 (2019)
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