Datasets:
gremlin97
/
RemoteSensingCorpus

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
820
[28] R. Shrestha, K. Kafle, and C. Kanan, “Answer them all! toward universal
visual question answering models,” in IEEE Conference on Computer
Vision and Pattern Recognition , 2019, pp. 10 472–10 481.
[29] J. P. Bigham, C. Jayant, H. Ji, G. Little, A. Miller, R. C. Miller, R. Miller,
A. Tatarowicz, B. White, S. White et al. , “VizWiz: nearly real-time
answers to visual questions,” in ACM symposium on User interface
software and technology . ACM, 2010, pp. 333–342.
[30] D. Gurari, Q. Li, A. Stangl, A. Guo, C. Lin, K. Grauman, J. Luo,
and J. Bigham, “VizWiz Grand Challenge: Answering visual questions
from blind people,” IEEE Conference on Computer Vision and Pattern
Recognition , 2018.
[31] J. Johnson, B. Hariharan, L. van der Maaten, L. Fei-Fei, C. Zitnick, and
R. Girshick, “CLEVR: A diagnostic dataset for compositional language
and elementary visual reasoning,” IEEE Conference on Computer Vision
and Pattern Recognition , 2017.
[32] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for
image recognition,” IEEE Conference on Computer Vision and Pattern
Recognition , 2016.
[33] S. Lobry and D. Tuia, “Deep Learning Models to Count Buildings
in High-Resolution Overhead Images,” in Joint Urban Remote Sensing
Event , 2019.
[34] R. Kiros, Y . Zhu, R. Salakhutdinov, R. Zemel, A. Torralba, R. Urtasun,
and S. Fidler, “Skip-thought vectors,” Neural Information Processing
Systems , 2015.
[35] Y . Zhu, R. Kiros, R. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba,
and S. Fidler, “Aligning books and movies: Towards story-like visual
explanations by watching movies and reading books,” in IEEE interna-
tional conference on computer vision , 2015, pp. 19–27.
[36] A. Li, Z. Lu, L. Wang, T. Xiang, and J.-R. Wen, “Zero-shot scene
classification for high spatial resolution remote sensing images,” IEEE
Transactions on Geoscience and Remote Sensing , vol. 55, no. 7, pp.
4157–4167, 2017.
[37] D. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
International Conference on Learning Representations , 2015.
[38] Y . Zhang, J. Hare, and A. Prgel-Bennett, “Learning to count objects
in natural images for visual question answering,” in International
Conference on Learning Representations , 2018.
[39] D. Tuia, C. Persello, and L. Bruzzone, “Domain adaptation for the
classification of remote sensing data: An overview of recent advances,”IEEE Geoscience and Remote Sensing Magazine , vol. 4, no. 2, pp. 41–
57, June 2016.
[40] W.-L. Chao, H. Hu, and F. Sha, “Cross-dataset adaptation for visual
question answering,” in IEEE Conference on Computer Vision and
Pattern Recognition , 2018, pp. 5716–5725.
[41] Y . Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh, “Making
the V in VQA matter: Elevating the role of image understanding in
Visual Question Answering,” in IEEE Conference on Computer Vision
and Pattern Recognition , 2017.
Sylvain Lobry (S16 - M17) received the Engi-
neering degree from Ecole pour l’Informatique et
les Techniques Avanc ´ees (EPITA), Kremlin Bic ˆetre,
France in 2013, the Masters degree in science and
technology from the University Pierre et Marie Cur-
rie (Paris 6), Paris in 2014, and the Ph.D. degree
from Telecom Paris, Paris, France, in 2017. He is
currently a Post-Doctoral Researcher with the Geo-
Information Science and Remote Sensing Labora-
tory, Wageningen University, The Netherlands. His
research interests include radar image processing
and multimodal processing of heterogeneous satellite data.
Diego Marcos Diego Marcos obtained an MSc
degree on Computational Sciences and Engineering
from the Ecole Polytechnique F ´ed´erale de Lausanne,
Switzerland, in 2014 and a Ph.D. degree in environ-
mental sciences from Wageningen University, The
Netherlands, in 2019. He is currently a Post-Doctoral
Researcher with the Geo-Information Science and
Remote Sensing Laboratory, at Wageningen Univer-
sity. His research interests include computer vision
and deep learning interpretability applied to geospa-
tial data.
Jesse Murray received an MSc degree in Geo-
Information Science from Wageningen University,
Wageningen, The Netherlands, in 2019. He is cur-
rently a Ph.D. candidate with the Geodetic Engineer-
ing Laboratory, at the Ecole Polytechnique Fdrale
de Lausanne, Switzerland. His research interests
include image processing and 3D geometry recon-
struction using computer vision and spatio-temporal
data.
Devis Tuia Devis Tuia (S07 - M09 - SM15) re-
ceived the Ph.D. degree from the University of
Lausanne, Lausanne, Switzerland, in 2009. He was
a Post-Doctoral Researcher in Valncia, Boulder, CO,
USA, and ´Ecole polytechnique f ´ed´erale de Lausanne
(EPFL), Lausanne. From 2014 to 2017, he was an
Assistant Professor with the University of Zurich,
Zrich, Switzerland. He is currently a Full Professor
with the Geo-Information Science and Remote Sens-
ing Laboratory, Wageningen University, Wagenin-
gen, The Netherlands. His research interests include
algorithms for data fusion of geospatial data (including remote sensing) using
machine learning and computer vision.
A Visual Active Search Framework for Geospatial Exploration
Anindya Sarkar1Michael Lanier1Scott Alfeld2Jiarui Feng1Roman Garnett1
Nathan Jacobs1Yevgeniy V orobeychik1
1{anindya,lanier.m,feng.jiarui,garnett,jacobsn,yvorobeychik }@wustl.edu,
2salfeld@amherst.edu
1Department of Computer Science & Engineering, Washington University in St. Louis
2Department of Computer Science, Amherst College
Abstract