Fetching the paper…
Reading the bibliography…
Learning representations through self-supervision on unlabeled data has proven highly effective for understanding diverse images.
M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort et al. , “Sentinel-2: Esa’s optical high-resolution mission for gmes operational services,” Remote Sens. Environ. , vol. 120, pp. 25–36, 2012
2012
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” Int. J. Comput. Vis. , vol. 115, pp. 211–252, 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
G. Cheng, J. Han, and X. Lu, “Remote sensing image scene classification: Benchmark and state of the art,” Proceedings of the IEEE , vol. 105, no. 10, pp. 1865–1883, 2017
2017
Earlier work this paper cites.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google earth engine: Planetary-scale geospatial analysis for everyone,” Remote Sens. Environ. , vol. 202, pp. 18–27, 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in ICCV , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang, “Dota: A large-scale dataset for object detection in aerial images,” in CVPR , 2018, pp. 3974–3983
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
G. Christie, N. Fendley, J. Wilson, and R. Mukherjee, “Functional map of the world,” in CVPR , 2018, pp. 6172–6180
2018
Earlier work this paper cites.
H. Zhao, Y. Zhang, S. Liu, J. Shi, C. C. Loy, D. Lin, and J. Jia, “Psanet: Point-wise spatial attention network for scene parsing,” in ECCV , 2018, pp. 267–283
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 12, no. 7, pp. 2217–2226, 2019
2019
Earlier work this paper cites.
N. Kermiche, “Contrastive hebbian feedforward learning for neural networks,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 31, no. 6, pp. 2118–2128, 2019
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in ICML , 2020, pp. 1597–1607
2020
Earlier work this paper cites.
Y. Tian, D. Krishnan, and P. Isola, “Contrastive multiview coding,” in ECCV , 2020, pp. 776–794
2020
Earlier work this paper cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in CVPR , 2020, pp. 9729–9738
2020
Earlier work this paper cites.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al. , “Bootstrap your own latent-a new approach to self-supervised learning,” in NeurIPS , 2020, pp. 21 271–21 284
2020
Earlier work this paper cites.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” in NeurIPS , 2020, pp. 9912–9924
2020
Earlier work this paper cites.
J. Kang, R. Fernandez-Beltran, P. Duan, S. Liu, and A. J. Plaza, “Deep unsupervised embedding for remotely sensed images based on spatially augmented momentum contrast,” IEEE Trans. Geosci. Remote Sens. , vol. 59, no. 3, pp. 2598–2610, 2020
2020
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2020
2020
Earlier work this paper cites.
O. Manas, A. Lacoste, X. Giró-i Nieto, D. Vazquez, and P. Rodriguez, “Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data,” in ICCV , 2021, pp. 9414–9423
2021
Cited alongside, same era.
K. Ayush, B. Uzkent, C. Meng, K. Tanmay, M. Burke, D. Lobell, and S. Ermon, “Geography-aware self-supervised learning,” in ICCV , 2021, pp. 10 181–10 190
2021
Cited alongside, same era.
X. Chen and K. He, “Exploring simple siamese representation learning,” in CVPR , 2021, pp. 15 750–15 758
2021
Cited alongside, same era.
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny, “Barlow twins: Self-supervised learning via redundancy reduction,” in ICML , 2021, pp. 12 310–12 320
2021
Cited alongside, same era.
Y. Liu, Z. Li, S. Pan, C. Gong, C. Zhou, and G. Karypis, “Anomaly detection on attributed networks via contrastive self-supervised learning,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 33, no. 6, pp. 2378–2392, 2021
I. Kalita and M. Roy, “Class-wise subspace alignment-based unsupervised adaptive land cover classification in scene-level using deep siamese network,” IEEE Trans. Neural Netw. Learn. Syst. , 2022
2022
Later among the works it cites.
A. K. Aksoy, M. Ravanbakhsh, and B. Demir, “Multi-label noise robust collaborative learning for remote sensing image classification,” IEEE Trans. Neural Netw. Learn. Syst. , 2022
2022
Later among the works it cites.
H. Li, Y. Li, G. Zhang, R. Liu, H. Huang, Q. Zhu, and C. Tao, “Global and local contrastive self-supervised learning for semantic segmentation of hr remote sensing images,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–14, 2022
2022
Later among the works it cites.
D. Muhtar, X. Zhang, and P. Xiao, “Index your position: A novel self-supervised learning method for remote sensing images semantic segmentation,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–11, 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
S. Vincenzi, A. Porrello, P. Buzzega, M. Cipriano, P. Fronte, R. Cuccu, C. Ippoliti, A. Conte, and S. Calderara, “The color out of space: learning self-supervised representations for earth observation imagery,” in ICPR , 2021, pp. 3034–3041
2021
Cited alongside, same era.
W. Li, H. Chen, and Z. Shi, “Semantic segmentation of remote sensing images with self-supervised multitask representation learning,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 14, pp. 6438–6450, 2021
2021
Cited alongside, same era.
V. Stojnic and V. Risojevic, “Self-supervised learning of remote sensing scene representations using contrastive multiview coding,” in CVPR , 2021, pp. 1182–1191
2021
Cited alongside, same era.
H. Jung, Y. Oh, S. Jeong, C. Lee, and T. Jeon, “Contrastive self-supervised learning with smoothed representation for remote sensing,” IEEE Geosci. Remote Sens. Lett. , vol. 19, pp. 1–5, 2021
2021
Cited alongside, same era.
W. Li, K. Chen, H. Chen, and Z. Shi, “Geographical knowledge-driven representation learning for remote sensing images,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–16, 2021
2021
Cited alongside, same era.
Y. Wu, J. Li, Y. Yuan, A. K. Qin, Q.-G. Miao, and M.-G. Gong, “Commonality autoencoder: Learning common features for change detection from heterogeneous images,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 33, no. 9, pp. 4257–4270, 2021
2021
Cited alongside, same era.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in ICCV , 2021, pp. 10 012–10 022
2021
Cited alongside, same era.
U. Mall, B. Hariharan, and K. Bala, “Change-aware sampling and contrastive learning for satellite images,” in CVPR , 2023, pp. 5261–5270
2023
Closest in time.
G. Wu, J. Jiang, and X. Liu, “A practical contrastive learning framework for single-image super-resolution,” IEEE Trans. Neural Netw. Learn. Syst. , 2023
2023
Closest in time.
J. Liu, X. Huang, J. Zheng, Y. Liu, and H. Li, “Mixmae: Mixed and masked autoencoder for efficient pretraining of hierarchical vision transformers,” in CVPR , 2023, pp. 6252–6261
2023
Closest in time.
Y. Yang, Y. Sun, S. Wang, J. Gao, F. Ju, and B. Yin, “A dual-masked deep structural clustering network with adaptive bidirectional information delivery,” IEEE Trans. Neural Netw. Learn. Syst. , 2023
2023
Closest in time.
M. Mendieta, B. Han, X. Shi, Y. Zhu, and C. Chen, “Towards geospatial foundation models via continual pretraining,” in ICCV , 2023, pp. 16 806–16 816
2023
Closest in time.
C. J. Reed, R. Gupta, S. Li, S. Brockman, C. Funk, B. Clipp, K. Keutzer, S. Candido, M. Uyttendaele, and T. Darrell, “Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning,” in ICCV , 2023, pp. 4088–4099
2023
Closest in time.
M. Tang, K. Georgiou, H. Qi, C. Champion, and M. Bosch, “Semantic segmentation in aerial imagery using multi-level contrastive learning with local consistency,” in WACV , 2023, pp. 3798–3807
2023
Closest in time.
F. Bastani, P. Wolters, R. Gupta, J. Ferdinando, and A. Kembhavi, “Satlaspretrain: A large-scale dataset for remote sensing image understanding,” in ICCV , 2023, pp. 16 772–16 782
2023
Closest in time.
C. Tao, J. Qi, G. Zhang, Q. Zhu, W. Lu, and H. Li, “Tov: The original vision model for optical remote sensing image understanding via self-supervised learning,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , 2023
2023
Closest in time.
D. Muhtar, X. Zhang, P. Xiao, Z. Li, and F. Gu, “Cmid: A unified self-supervised learning framework for remote sensing image understanding,” IEEE Trans. Geosci. Remote Sens. , 2023
2023
Closest in time.
T. Yan, S. Zhao, M. Hu, M. Wang, X. Zhang, Z. Luo, and M. Wang, “Hcl: A hierarchical contrastive learning framework for zero-shot relation extraction,” IEEE Trans. Neural Netw. Learn. Syst. , 2024
2024
Closest in time.
S. Fu, Q. Peng, Y. He, X. Wang, B. Zou, D. Xu, X.-Y. Jing, and X. You, “Multilevel contrastive graph masked autoencoders for unsupervised graph-structure learning,” IEEE Trans. Neural Netw. Learn. Syst. , 2024
2024
Closest in time.
A. Gupta, J. Wu, J. Deng, and F.-F. Li, “Siamese masked autoencoders,” in NeurIPS , 2024
2024
Closest in time.
Z. Huang, M. Zhang, Y. Gong, Q. Liu, and Y. Wang, “Generic knowledge boosted pre-training for remote sensing images,” IEEE Trans. Geosci. Remote Sens. , 2024
2024
Closest in time.
J. Qi, Z. Gong, X. Liu, C. Chen, and P. Zhong, “Masked spatial–spectral autoencoders are excellent hyperspectral defenders,” IEEE Trans. Neural Netw. Learn. Syst. , 2024
2024
Closest in time.
D. Hong, B. Zhang, X. Li, Y. Li, C. Li, J. Yao, N. Yokoya, H. Li, P. Ghamisi, X. Jia et al. , “Spectralgpt: Spectral remote sensing foundation model,” IEEE Trans. Pattern Anal. Mach. Intell. , 2024
2024
Closest in time.
2024
Closest in time.
M. Tang, A. Cozma, K. Georgiou, and H. Qi, “Cross-scale mae: A tale of multiscale exploitation in remote sensing,” in NeurIPS , 2024
2024
Closest in time.
J. Qu, W. Dong, Y. Yang, T. Zhang, Y. Li, and Q. Du, “Cycle-refined multidecision joint alignment network for unsupervised domain adaptive hyperspectral change detection,” IEEE Trans. Neural Netw. Learn. Syst. , 2024
2024
Closest in time.