Fetching the paper…
Reading the bibliography…
Domain adaptive semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
Benchmarking robustness in object detection: Autonomous driving when winter is coming
Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A. S., Bethge, M., and Brendel, W. (2019) · 1907
Earlier work this paper cites.
Fftw: An adaptive software architecture for the fft
Frigo, M. and Johnson, S. G. (1998) · 1998
Earlier work this paper cites.
Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F. (2006) · 2006
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten, G. H. (2008) · 2008
Earlier work this paper cites.
Optimal kernel choice for large-scale two-sample tests
Gretton, A., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K., and Sriperumbudur, B. K. (2012) · 2012
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. (2016) · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Earlier work this paper cites.
Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
Hoffman, J., Wang, D., Yu, F., and Darrell, T. (2016) · 2016
Earlier work this paper cites.
Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I. (2016) · 2016
Earlier work this paper cites.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R. (2016) · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games
Richter, S. R., Vineet, V., Roth, S., and Koltun, V. (2016) · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Return of frustratingly easy domain adaptation
Sun, B., Feng, J., and Saenko, K. (2016) · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K. (2016) · 2016
Earlier work this paper cites.
Unsupervised pixel-level domain adaptation with generative adversarial networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., and Krishnan, D. (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., and Yuille, A. L. (2017) · 2017
Earlier work this paper cites.
Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X. and Belongie, S. (2017) · 2017
Earlier work this paper cites.
Unified deep supervised domain adaptation and generalization
Motiian, S., Piccirilli, M., Adjeroh, D. A., and Doretto, G. (2017) · 2017
Earlier work this paper cites.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H. (2017) · 2017
Earlier work this paper cites.
Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T. (2017) · 2017
Earlier work this paper cites.
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017) · 2017
Earlier work this paper cites.
Scene parsing through ade20k dataset
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A. (2017) · 2017
Earlier work this paper cites.
Metareg: Towards domain generalization using meta-regularization
Balaji, Y., Sankaranarayanan, S., and Chellappa, R. (2018) · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Conditional generative adversarial network for structured domain adaptation
Hong, W., Wang, Z., Yang, M., and Yuan, J. (2018) · 2018
Earlier work this paper cites.
Conditional adversarial domain adaptation
Long, M., Cao, Z., Wang, J., and Jordan, M. I. (2018) · 2018
Earlier work this paper cites.
Image to image translation for domain adaptation
Murez, Z., Kolouri, S., Kriegman, D., Ramamoorthi, R., and Kim, K. (2018) · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O. (2018) · 2018
Cited alongside, same era.
Two at once: Enhancing learning and generalization capacities via ibn-net
Pan, X., Luo, P., Shi, J., and Tang, X. (2018) · 2018
Cited alongside, same era.
Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., and Harada, T. (2018) · 2018
Cited alongside, same era.
Learning from synthetic data: Addressing domain shift for semantic segmentation
Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S. N., and Chellappa, R. (2018) · 2018
Cited alongside, same era.
Unsupervised intra-domain adaptation for semantic segmentation through self-supervision
Pan, F., Shin, I., Rameau, F., Lee, S., and Kweon, I. S. (2020) · 2020
Later among the works it cites.
Correlation-aware adversarial domain adaptation and generalization
Rahman, M. M., Fookes, C., Baktashmotlagh, M., and Sridharan, S. (2020) · 2020
Later among the works it cites.
Two-phase pseudo label densification for self-training based domain adaptation
Shin, I., Woo, S., Pan, F., and Kweon, I. S. (2020) · 2020
Later among the works it cites.
Fda: Fourier domain adaptation for semantic segmentation
Yang, Y. and Soatto, S. (2020) · 2020
Later among the works it cites.
Self-supervised augmentation consistency for adapting semantic segmentation
Araslanov, N. and Roth, S. (2021) · 2021
Later among the works it cites.
Emerging properties in self-supervised vision transformers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker, M. (2018) · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Domain generalization via model-agnostic learning of semantic features
Dou, Q., Coelho de Castro, D., Kamnitsas, K., and Glocker, B. (2019) · 2019
Cited alongside, same era.
Iterative normalization: Beyond standardization towards efficient whitening
Huang, L., Zhou, Y., Zhu, F., Liu, L., and Shao, L. (2019) · 2019
Cited alongside, same era.
Style augmentation: data augmentation via style randomization
Jackson, P. T., Abarghouei, A. A., Bonner, S., Breckon, T. P., and Obara, B. (2019) · 2019
Cited alongside, same era.
Sliced wasserstein discrepancy for unsupervised domain adaptation
Lee, C.-Y., Batra, T., Baig, M. H., and Ulbricht, D. (2019) · 2019
Cited alongside, same era.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A. (2021) · 2021
Later among the works it cites.
Exploring simple siamese representation learning
Chen, X. and He, K. (2021) · 2021
Later among the works it cites.
Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening
Choi, S., Jung, S., Yun, H., Kim, J. T., Kim, S., and Choo, J. (2021) · 2021
Later among the works it cites.
Region-aware contrastive learning for semantic segmentation
Hu, H., Cui, J., and Wang, L. (2021) · 2021
Later among the works it cites.
Generalize then adapt: Source-free domain adaptive semantic segmentation
Kundu, J. N., Kulkarni, A., Singh, A., Jampani, V., and Babu, R. V. (2021) · 2021
Later among the works it cites.
Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm
Li, Y., Liang, F., Zhao, L., Cui, Y., Ouyang, W., Shao, J., Yu, F., and Yan, J. (2021) · 2021
Later among the works it cites.
Category-level adversarial adaptation for semantic segmentation using purified features
Luo, Y., Liu, P., Zheng, L., Guan, T., Yu, J., and Yang, Y. (2021) · 2021
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2021) · 2021
Later among the works it cites.
Dacs: Domain adaptation via cross-domain mixed sampling
Tranheden, W., Olsson, V., Pinto, J., and Svensson, L. (2021) · 2021
Later among the works it cites.
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
Later among the works it cites.
Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation
Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., and Wen, F. (2021) · 2021
Later among the works it cites.
Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation
Zheng, Z. and Yang, Y. (2021) · 2021
Later among the works it cites.
Maximizing cosine similarity between spatial features for unsupervised domain adaptation in semantic segmentation
Chung, I., Kim, D., and Kwak, N. (2022) · 2022
Later among the works it cites.
Category contrast for unsupervised domain adaptation in visual tasks
Huang, J., Guan, D., Xiao, A., Lu, S., and Shao, L. (2022) · 2022
Later among the works it cites.
Class-balanced pixel-level self-labeling for domain adaptive semantic segmentation
Li, R., Li, S., He, C., Zhang, Y., Jia, X., and Zhang, L. (2022) · 2022
Later among the works it cites.
Bootstrapping semantic segmentation with regional contrast
Liu, S., Zhi, S., Johns, E., and Davison, A. J. (2022) · 2022
Later among the works it cites.
Semantic-aware domain generalized segmentation
Peng, D., Lei, Y., Hayat, M., Guo, Y., and Li, W. (2022) · 2022
Later among the works it cites.
Semi-supervised semantic segmentation using unreliable pseudo labels
Wang, Y., Wang, H., Shen, Y., Fei, J., Li, W., Jin, G., Wu, L., Zhao, R., and Le, X. (2022) · 2022
Later among the works it cites.
Style-hallucinated dual consistency learning for domain generalized semantic segmentation
Zhao, Y., Zhong, Z., Zhao, N., Sebe, N., and Lee, G. H. (2022) · 2022
Later among the works it cites.
Domain adaptive semantic segmentation with regional contrastive consistency regularization
Zhou, Q., Zhuang, C., Lu, X., and Ma, L. (2022) · 2022
Later among the works it cites.
Mic: Masked image consistency for context-enhanced domain adaptation
Hoyer, L., Dai, D., Wang, H., and Van Gool, L. (2023) · 2023
Closest in time.
Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation
Xie, B., Li, S., Li, M., Liu, C. H., Huang, G., and Wang, G. (2023) · 2023
Closest in time.
Adding conditional control to text-to-image diffusion models
Zhang, L., Rao, A., and Agrawala, M. (2023) · 2023
Closest in time.
Reconstructive visual instruction tuning
Wang, H., Zheng, A., Zhao, Y., Wang, T., Zheng, G., Zhang, X., and Zhang, Z. (2024) · 2024
Closest in time.