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The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to leverage a large amount of unlabeled or weakly labeled data.
Semi-supervised learning by entropy minimization
Grandvalet, Y. and Bengio, Y. (2004) · 2004
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Keep it simple: Image statistics matching for domain adaptation
Abramov, A., Bayer, C., and Heller, C. (2020) · 2005
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. (2010) · 2010
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Semantic contours from inverse detectors
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., and Malik, J. (2011) · 2011
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Efficient inference in fully connected crfs with gaussian edge potentials
Krähenbühl, P. and Koltun, V. (2011) · 2011
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H. et al. (2013) · 2013
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Learning with pseudo-ensembles
Bachman, P., Alsharif, O., and Precup, D. (2014) · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Dai, J., He, K., and Sun, J. (2015) · 2015
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Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation
Papandreou, G., Chen, L.-C., Murphy, K. P., and Yuille, A. L. (2015) · 2015
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From image-level to pixel-level labeling with convolutional networks
Pinheiro, P. O. and Collobert, R. (2015) · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
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Multi-scale context aggregation by dilated convolutions
Yu, F. and Koltun, V. (2015) · 2015
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
Hoffman, J., Wang, D., Yu, F., and Darrell, T. (2016) · 2016
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Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
Lin, D., Dai, J., Jia, J., He, K., and Sun, J. (2016) · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R. (2016) · 2016
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Playing for data: Ground truth from computer games
Richter, S. R., Vineet, V., Roth, S., and Koltun, V. (2016) · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A. M. (2016) · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M., Javanmardi, M., and Tasdizen, T. (2016) · 2016
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Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016) · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., and Cipolla, R. (2017) · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W. (2017) · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F. (2017) · 2017
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Combining bottom-up, top-down, and smoothness cues for weakly supervised image segmentation
Roy, A. and Todorovic, S. (2017) · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H. (2017) · 2017
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Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
Wei, Y., Feng, J., Liang, X., Cheng, M.-M., Zhao, Y., and Yan, S. (2017) · 2017
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Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017) · 2017
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Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation
Ahn, J. and Kwak, S. (2018) · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018) · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A., and Darrell, T. (2018) · 2018
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Conditional generative adversarial network for structured domain adaptation
Hong, W., Wang, Z., Yang, M., and Yuan, J. (2018) · 2018
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Image to image translation for domain adaptation
Murez, Z., Kolouri, S., Kriegman, D., Ramamoorthi, R., and Kim, K. (2018) · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O. (2018) · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., and Harada, T. (2018) · 2018
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Learning from synthetic data: Addressing domain shift for semantic segmentation
Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S. N., and Chellappa, R. (2018) · 2018
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Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank
Alonso, I., Sabater, A., Ferstl, D., Montesano, L., and Murillo, A. C. (2021) · 2021
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Self-supervised augmentation consistency for adapting semantic segmentation
Araslanov, N. and Roth, S. (2021) · 2021
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Per-pixel classification is not all you need for semantic segmentation
Cheng, B., Schwing, A., and Kirillov, A. (2021) · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2021) · 2021
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Semi-supervised semantic segmentation via adaptive equalization learning
Hu, H., Wei, F., Hu, H., Ye, Q., Cui, J., and Wang, L. (2021) · 2021
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Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker, M. (2018) · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Zou, Y., Yu, Z., Kumar, B., and Wang, J. (2018) · 2018
Cited alongside, same era.
All about structure: Adapting structural information across domains for boosting semantic segmentation
Chang, W.-L., Wang, H.-P., Peng, W.-H., and Chiu, W.-C. (2019) · 2019
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Progressive feature alignment for unsupervised domain adaptation
Chen, C., Xie, W., Huang, W., Rong, Y., Ding, X., Huang, Y., Xu, T., and Huang, J. (2019) · 2019
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Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation
Choi, J., Kim, T., and Kim, C. (2019) · 2019
Cited alongside, same era.
Dlow: Domain flow for adaptation and generalization
Gong, R., Li, W., Chen, Y., and Gool, L. V. (2019) · 2019
Cited alongside, same era.
Integral object mining via online attention accumulation
Jiang, P.-T., Hou, Q., Cao, Y., Cheng, M.-M., Wei, Y., and Xiong, H.-K. (2019) · 2019
Cited alongside, same era.
Discriminative region suppression for weakly-supervised semantic segmentation
Kim, B., Han, S., and Kim, J. (2021) · 2021
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Bootstrapping semantic segmentation with regional contrast
Liu, S., Zhi, S., Johns, E., and Davison, A. J. (2021) · 2021
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Pixmatch: Unsupervised domain adaptation via pixelwise consistency training
Melas-Kyriazi, L. and Manrai, A. K. (2021) · 2021
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Classmix: Segmentation-based data augmentation for semi-supervised learning
Olsson, V., Tranheden, W., Pinto, J., and Svensson, L. (2021) · 2021
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Acdc: The adverse conditions dataset with correspondences for semantic driving scene understanding
Sakaridis, C., Dai, D., and Van Gool, L. (2021) · 2021
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Segmenter: Transformer for semantic segmentation
Strudel, R., Garcia, R., Laptev, I., and Schmid, C. (2021) · 2021
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Context decoupling augmentation for weakly supervised semantic segmentation
Su, Y., Sun, R., Lin, G., and Wu, Q. (2021) · 2021
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Ecs-net: Improving weakly supervised semantic segmentation by using connections between class activation maps
Sun, K., Shi, H., Zhang, Z., and Huang, Y. (2021) · 2021
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Exploring cross-image pixel contrast for semantic segmentation
Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., and Van Gool, L. (2021) · 2021
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Embedded discriminative attention mechanism for weakly supervised semantic segmentation
Wu, T., Huang, J., Gao, G., Wei, X., Wei, X., Luo, X., and Liu, C. H. (2021) · 2021
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Segformer: Simple and efficient design for semantic segmentation with transformers
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., and Luo, P. (2021) · 2021
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Non-salient region object mining for weakly supervised semantic segmentation
Yao, Y., Chen, T., Xie, G.-S., Zhang, C., Shen, F., Wu, Q., Tang, Z., and Zhang, J. (2021) · 2021
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A simple baseline for semi-supervised semantic segmentation with strong data augmentation
Yuan, J., Liu, Y., Shen, C., Wang, Z., and Li, H. (2021) · 2021
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Contrastive learning for label efficient semantic segmentation
Zhao, X., Vemulapalli, R., Mansfield, P. A., Gong, B., Green, B., Shapira, L., and Wu, Y. (2021) · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H., et al. (2021) · 2021
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Pixel contrastive-consistent semi-supervised semantic segmentation
Zhong, Y., Yuan, B., Wu, H., Yuan, Z., Peng, J., and Wang, Y.-X. (2021) · 2021
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Domain adaptive semantic segmentation with regional contrastive consistency regularization
Zhou, Q., Zhuang, C., Lu, X., and Ma, L. (2021) · 2021
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Self-training with differentiable teacher
Zuo, S., Yu, Y., Liang, C., Jiang, H., Er, S., Zhang, C., Zhao, T., and Zha, H. (2021) · 2021
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Masked-attention mask transformer for universal image segmentation
Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., and Girdhar, R. (2022) · 2022
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Pointly-supervised panoptic segmentation
Fan, J., Zhang, Z., and Tan, T. (2022) · 2022
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Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation
Hoyer, L., Dai, D., and Van Gool, L. (2022) · 2022
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Learning affinity from attention: end-to-end weakly-supervised semantic segmentation with transformers
Ru, L., Zhan, Y., Yu, B., and Du, B. (2022) · 2022
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Groupvit: Semantic segmentation emerges from text supervision
Xu, J., De Mello, S., Liu, S., Byeon, W., Breuel, T., Kautz, J., and Wang, X. (2022) · 2022
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St++: Make self-training work better for semi-supervised semantic segmentation
Yang, L., Zhuo, W., Qi, L., Shi, Y., and Gao, Y. (2022) · 2022
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Segment anything is not always perfect: An investigation of sam on different real-world applications
Ji, W., Li, J., Bi, Q., Liu, T., Li, W., and Cheng, L. (2023) · 2023
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Segment anything
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023
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Token contrast for weakly-supervised semantic segmentation
Ru, L., Zheng, H., Zhan, Y., and Du, B. (2023) · 2023
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Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., and Wang, B. (2024) · 2024
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