Fetching the paper…
Reading the bibliography…
Do we on the right way for remote sensing image understanding (RSIU) by training models via supervised data-dependent and task-dependent way, instead of human vision in a label-free and task-independent way? We argue that a more desirable RSIU model should be trained with intrinsic structure from data rather that extrinsic human labels to realize generalizability across a wide range of RSIU tasks.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei, Language models are few-shot learners, in: Proceedings of the Advances in Neural Information Processing Systems (NIPS), Vol. 33, Curran Associates, Inc., 2020, pp. 1877–1901
1901
Earlier work this paper cites.
Y. Tian, D. Krishnan, P. Isola, Contrastive multiview coding, arXiv preprint arXiv:1906.05849
1906
Earlier work this paper cites.
C. Fellbaum, G. Miller, WordNet: An Electronic Lexical Database, MIT press, 1998
1998
Earlier work this paper cites.
2003
Earlier work this paper cites.
doi:10.1023/B:VISI.0000022288.19776.77
P. F. Felzenszwalb, D. P. Huttenlocher, Efficient graph-based image segmentation, International Journal of Computer Vision 59 (2) (2004) 167–181 · 2004
Earlier work this paper cites.
2006
Earlier work this paper cites.
doi:10.1145/1869790.1869829
Y. Yang, S. Newsam, Bag-of-visual-words and spatial extensions for land-use classification, in: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’10, ACM Press, 2010, p. 270 · 2010
Earlier work this paper cites.
doi:10.5194/isprsannals-I-3-293-2012
F. Rottensteiner, G. Sohn, J. Jung, M. Gerke, C. Baillard, S. Benitez, U. Breitkopf, The isprs benchmark on urban object classification and 3d building reconstruction, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I–3 (1) (2012) 293–298 · 2012
Earlier work this paper cites.
doi:10.1007/s11263-013-0620-5
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, A. W. M. Smeulders, Selective search for object recognition, International Journal of Computer Vision 104 (2) (2013) 154–171 · 2013
Earlier work this paper cites.
doi:10.1038/514434c
C. Jun, Y. Ban, S. Li, Open access to earth land-cover map, Nature 514 (7523) (2014) 434–434 · 2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks?, in: Proceedings of the Advances in Neural Information Processing Systems (NIPS), NIPS’14, MIT Press, 2014, p. 3320–3328
2014
Earlier work this paper cites.
doi:10.1109/CVPR.2015.7298965
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015, pp. 3431–3440 · 2015
Earlier work this paper cites.
doi:10.1007/s11263-015-0816-y
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, L. Fei-Fei, Imagenet large scale visual recognition challenge, International Journal of Computer Vision 115 (3) (2015) 211–252 · 2015
Earlier work this paper cites.
doi:10.1109/TPAMI.2016.2577031
S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6) (2017) 1137–1149 · 2016
Earlier work this paper cites.
doi:10.1109/JPROC.2017.2675998
G. Cheng, J. Han, X. Lu, Remote sensing image scene classification: Benchmark and state of the art, Proceedings of the IEEE 105 (10) (2017) 1865–1883 · 2017
Earlier work this paper cites.
doi:10.1109/ICCV.2017.97
C. Sun, A. Shrivastava, S. Singh, A. Gupta, Revisiting unreasonable effectiveness of data in deep learning era, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, 2017, p. 843–852 · 2017
Cited alongside, same era.
doi:10.1109/TGRS.2017.2685945
G.-S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, X. Lu, Aid: A benchmark data set for performance evaluation of aerial scene classification, IEEE Transactions on Geoscience and Remote Sensing 55 (7) (2017) 3965–3981 · 2017
Cited alongside, same era.
doi:10.3390/rs9070725
Z. Xiao, Y. Long, D. Li, C. Wei, G. Tang, J. Liu, High-resolution remote sensing image retrieval based on cnns from a dimensional perspective, Remote Sensing 9 (7) (2017) 725 · 2017
Cited alongside, same era.
doi:10.1109/TIP.2017.2773199
Z. Zou, Z. Shi, Random access memories: A new paradigm for target detection in high resolution aerial remote sensing images, IEEE Transactions on Image Processing 27 (3) (2018) 1100–1111 · 2017
Cited alongside, same era.
doi:10.3390/rs10060964
Z. Shao, K. Yang, W. Zhou, Performance evaluation of single-label and multi-label remote sensing image retrieval using a dense labeling dataset, Remote Sensing 10 (6) (2018) 964 · 2018
Cited alongside, same era.
doi:10.3390/s20061594
H. Li, X. Dou, C. Tao, Z. Wu, J. Chen, J. Peng, M. Deng, L. Zhao, Rsi-cb: A large-scale remote sensing image classification benchmark using crowdsourced data, Sensors 20 (66) (2020) 1594 · 2020
Later among the works it cites.
doi:10.1016/j.isprsjprs.2020.04.019
Z. Zheng, Y. Zhong, A. Ma, X. Han, J. Zhao, Y. Liu, L. Zhang, Hynet: Hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery, ISPRS Journal of Photogrammetry and Remote Sensing 166 (2020) 1–14 · 2020
Later among the works it cites.
doi:10.1109/TPAMI.2020.2992393
L. Jing, Y. Tian, Self-supervised visual feature learning with deep neural networks: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (11) (2021) 4037–4058 · 2020
Later among the works it cites.
doi:10.1109/LGRS.2020.3038420
C. Tao, J. Qi, W. Lu, H. Wang, H. Li, Remote sensing image scene classification with self-supervised paradigm under limited labeled samples, IEEE Geoscience and Remote Sensing Letters 19 (2022) 1–5 · 2020
Later among the works it cites.
doi:10.1109/TPAMI.2020.3031898
G.-J. Qi, J. Luo, Small data challenges in big data era: A survey of recent progress on unsupervised and semi-supervised methods, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (4) (2022) 2168–2187 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
doi:10.1109/CVPR.2018.00418
G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, L. Zhang, Dota: A large-scale dataset for object detection in aerial images, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2018, p. 3974–3983 · 2018
Cited alongside, same era.
doi:10.1016/j.isprsjprs.2018.01.004
W. Zhou, S. Newsam, C. Li, Z. Shao, Patternnet: A benchmark dataset for performance evaluation of remote sensing image retrieval, ISPRS Journal of Photogrammetry and Remote Sensing 145 (2018) 197–209 · 2018
Cited alongside, same era.
doi:10.1109/CVPR.2018.00646
G. Christie, N. Fendley, J. Wilson, R. Mukherjee, Functional map of the world, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2018, p. 6172–6180 · 2018
Cited alongside, same era.
doi:10.3934/mfc.2018008
Z. Qin, F. Yu, C. Liu, X. Chen, How convolutional neural networks see the world – A survey of convolutional neural network visualization methods, Mathematical Foundations of Computing 1 (2) (2018) 149 · 2018
Cited alongside, same era.
doi:10.1109/CVPRW.2018.00031
I. Demir, K. Koperski, D. Lindenbaum, G. Pang, J. Huang, S. Basu, F. Hughes, D. Tuia, R. Raskar, Deepglobe 2018: A challenge to parse the earth through satellite images, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2018, p. 172–17209 · 2018
Cited alongside, same era.
doi:10.1016/j.isprsjprs.2019.10.001
C. Tao, J. Qi, Y. Li, H. Wang, H. Li, Spatial information inference net: Road extraction using road-specific contextual information, ISPRS Journal of Photogrammetry and Remote Sensing 158 (2019) 155–166 · 2019
Cited alongside, same era.
doi:10.1109/ICCV.2019.00502
K. He, R. Girshick, P. Dollar, Rethinking imagenet pre-training, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, 2019, p. 4917–4926 · 2019
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, G. Hinton, A simple framework for contrastive learning of visual representations, in: Proceedings of the International Conference on Machine Learning (ICML), Vol. 119 of Proceedings of Machine Learning Research, PMLR, 2020, p. 1597–1607
2020
Later among the works it cites.
doi:10.1109/CVPR42600.2020.00975
K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020, p. 9726–9735 · 2020
Later among the works it cites.
doi:10.1016/j.rse.2021.112830
W. Lu, C. Tao, H. Li, J. Qi, Y. Li, A unified deep learning framework for urban functional zone extraction based on multi-source heterogeneous data, Remote Sensing of Environment 270 (1) (2022) 112830 · 2021
Later among the works it cites.
X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, J. Tang, Self-supervised learning: Generative or contrastive, IEEE Transactions on Knowledge and Data Engineering (2021) 1–1
2021
Later among the works it cites.
J. Li, P. Zhou, C. Xiong, S. Hoi, Prototypical contrastive learning of unsupervised representations, in: International Conference on Learning Representations (ICLR), 2021
2021
Later among the works it cites.
doi:10.1109/CVPR46437.2021.00119
M. Yang, Y. Li, Z. Huang, Z. Liu, P. Hu, X. Peng, Partially view-aligned representation learning with noise-robust contrastive loss, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, p. 1134–1143 · 2021
Later among the works it cites.
doi:10.1109/CVPR46437.2021.01071
C. Wei, K. Sohn, C. Mellina, A. Yuille, F. Yang, Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2021, p. 10852–10861 · 2021
Later among the works it cites.
doi:10.1109/JSTARS.2021.3063096
Z. Zhou, S. Li, W. Wu, W. Guo, X. Li, G. Xia, Z. Zhao, Nasc-tg2: Natural scene classification with tiangong-2 remotely sensed imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021) 3228–3242 · 2021
Later among the works it cites.
doi:10.1109/TGRS.2022.3147513
H. Li, Y. Li, G. Zhang, R. Liu, H. Huang, Q. Zhu, C. Tao, Global and local contrastive self-supervised learning for semantic segmentation of hr remote sensing images, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1–14 · 2022
Closest in time.