text stringlengths 0 820 |
|---|
aligned representations ,” 2017, arXiv:1706.00932. |
[9] J.-M. Perez-Rua , V. Vielzeuf , S. Pateux , M. Baccouche , and F. Jurie, |
“MFAS: Multimodal fusion architecture search ,” in Proc. IEEE/CVF |
Conf. Comput. Vis. Pattern Recognit. (CVPR), June 2019 , pp. 6966 –6975. |
[10] A. R. Zamir et al., “Robust learning through cross-task consisten - |
cy,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2020 , |
pp. 11,197 –11,206 . |
[11] A. Agrawal et al., “VQA: Visual question answering ,” in Proc. Int. |
Conf. Comput. Vis. (ICCV) , 2015 , pp. 2425 –2433 . |
[12] S. Lobry , D. Marcos , J. Murray , and D. Tuia, “RSVQA: Visual |
question answering for remote sensing data ,” IEEE Trans. Geosci. |
Remote Sens. , vol. 58, no. 12, pp. 8555–8566, Dec. 2020, doi: |
10.1109/TGRS.2020.2988782. |
[13] A. Karpatne , W. Watkins , J. Read , and V. Kumar , “Physics-guid - |
ed neural networks (pgnn): An application in lake temperature |
modeling ,” 2017, arXiv:1710.11431.[14] R. Stewart and S. Ermon , “Label-free supervision of neural net - |
works with physics and domain knowledge ,” in Proc. 35th AAAI |
Conf. Artif. Intell., AAAI Press , 2017, pp. 2576 –2582 . |
[15] M. Raissi , P. Perdikaris , and G. Karniadakis , “Physics-informed |
neural networks: A deep learning framework for solving forward |
and inverse problems involving nonlinear partial differential |
equations ,” Comput. Phys. , vol. 378, pp. 686–707, Feb. 2019 . doi: |
10.1016/j.jcp.2018.10.045 . |
[16] G. Camps-Valls et al., “Physics-aware Gaussian processes in re - |
mote sensing ,” Appl. Soft Comput. , vol. 68, pp. 69–82, July 2018 . |
doi: 10.1016/j.asoc.2018.03.021 . |
[17] R. Roscher , B. Bohn , M. F. Duarte , and J. Garcke , “Explainable ma - |
chine learning for scientific insights and discoveries ,” IEEE Access , vol. |
8, pp. 42,200 –42,216 , Feb. 2020 . doi: 10.1109/ACCESS.2020.2976199 . |
[18] W. Samek , G. Montavon , S. Lapuschkin , C. J. Anders , and K.- |
R. Múller , “Toward interpretable machine learning: Transparent |
deep neural networks and beyond ,” 2020 , arXiv:2003.07631. |
[19] J. Pearl and D. Mackenzie , The Book of Why . New York : Basic |
Books , 2018 . |
[20] J. Peters , D. Janzing , and, and B. Schölkopf , Elements of Causal In - |
ference - Foundations and Learning Algorithms, ser. Adaptive Computa - |
tion and Machine Learning Series . Cambridge, MA : MIT Press , 2017. |
[21] A. Pérez-Suay and G. Camps-Valls , “Causal inference in geosci - |
ence and remote sensing from observational data ,” IEEE Trans. |
Geosci. Remote Sens. , vol. 57, no. 3, pp. 1502 –1513 , 2019 . doi: |
10.1109/TGRS.2018.2867002 . |
[22] J. Runge et al., “Inferring causation from time series with perspec - |
tives in Earth system sciences ,” Nature Commun. , vol. 10, no. 1, |
p. 2533, 2019. doi: 10.1038/s41467-019-10105-3 . |
[23] M. T. Ribeiro , S. Singh , and C. Guestrin , “‘Why should I trust |
you?’: Explaining the predictions of any classifier ,” in Proc. 22nd |
ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining , 2016 , |
pp. 1135 –1144 . doi: 10.1145/2939672.2939778 . |
[24] B. M. Lake , T. D. Ullman , J. B. Tenenbaum , and S. J. Gershman , |
“Building machines that learn and think like people ,” Nov. 2016 , |
arXiv: 1604.00289. |
[25] A. Santoro et al., “A simple neural network module for relational rea - |
soning ,” in Proc. Adv. Neural Inf. Process. Syst. , 2017, pp. 4967–4976 . |
[26] B. Zhou , A. Andonian , A. Oliva , and A. Torralba , “Temporal rela - |
tional reasoning in videos ,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , |
2018 , pp. 803–818. |
[27] X. Zhang , X. Wang , X. Tang, H. Zhou , and C. Li, “Description genera - |
tion for remote sensing images using attribute attention mechanism ,” |
Remote Sens. , vol. 11, no. 6, p. 612, Mar 2019 . doi: 10.3390/rs11060612 . |
[28] D. Hu et al., “Cross-task transfer for multimodal aerial scene rec - |
ognition ,” 2020 , arXiv:2005.08449. |
[29] S. Lefèvre , D. Tuia, J. D. Wegner , T. Produit , and A. S. Nassar , |
“Towards seamless multi-view scene analysis from satellite to |
street-level ,” Proc. IEEE , vol. 105, no. 10, pp. 1884 –1899 , 2017. |
doi: 10.1109/JPROC.2017.2684300 . |
[30] S. Workman , M. Zhai, D. Crandall , and N. Jacobs , “A unified |
model for near and remote sensing ,” in Proc. IEEE Int. Conf. Com - |
put. Vis. (ICCV) , 2017, pp. 1–10. |
[31] J. Kang , M. Körner , Y. Wang , H. Taubenböck , and X. X. Zhu, |
“Building instance classification using street view images ,” |
ISPRS J. Photogram. Remote Sens. , vol. 145, pp. 44–59, Nov. 2018 . |
Authorized licensed use limited to: ASU Library. Downloaded on March 07,2024 at 22:07:36 UTC from IEEE Xplore. Restrictions apply. |
103 |
JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE [Online]. Available: http://www.sciencedirect.com/science/arti - |
cle/pii/S0924271618300352 . doi: 10.1016/j.isprsjprs.2018.02.006 . |
[32] S. Srivastava , J. E. Vargas-Muñoz , and D. Tuia, “Understanding |
urban landuse from the above and ground perspectives: A deep |
learning, multimodal solution ,” Remote Sens. Environ. , vol. 228, |
pp. 129–143, July 2019 . doi: 10.1016/j.rse.2019.04.014 . |
[33] S. Salcedo-Sanz et al., “Machine learning information fusion in |
earth observation: A comprehensive review of methods, applica - |
tions and data sources ,” Inf. Fusion , vol. 63, pp. 256–272, Nov. |
2020 . doi: 10.1016/j.inffus.2020.07.004 . |
[34] S. Workman , R. Souvenir , and N. Jacobs , “Wide-area image |
geolocalization with aerial reference imagery ,” in Proc. IEEE Int. |
Conf. Comput. Vis. (ICCV) , 2015 , pp. 3961 –3969 . |
[35] T. Salem , S. Workman , and N. Jacobs , “Learning a dynamic map |
of visual appearance ,” in Proc. IEEE Conf. Comput. Vis. Pattern |
Recognit. (CVPR) , 2020 , pp. 12,435 –12,444 . |
[36] R. Kitchin , “Big data and human geography: Opportunities, |
challenges and risks ,” Dialogues Human Geogr. , vol. 3, no. 3, |
pp. 262–267, 2013 . doi: 10.1177/2043820613513388 . |
[37] J. E. Vargas , S. Srivastava , D. Tuia, and A. X. Falcão , “OpenStreet - |
Map: Challenges and opportunities in machine learning and |
remote sensing ,” IEEE Geosci. Remote Sens. Mag. , vol. 9, no. 1, |
pp. 184–199, 2021. |
[38] M. Zhai, T. Salem , C. Greenwell , S. Workman , R. Pless , and N. Ja- |
cobs, “Learning geo-temporal image features ,” in Proc. Brit. Mach. |
Vis. Conf. (BMVC) , 2018 , pp. 1–12. |
[39] J. Lin, L. Mou, T. Yu, X. Zhu, and Z. J. Wang , “Dual adversarial |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.