David Crandall
David Crandall
Professor of Computer Science, Indiana University
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Mapping the world's photos
DJ Crandall, L Backstrom, D Huttenlocher, J Kleinberg
Proceedings of the 18th international conference on World wide web, 761-770, 2009
Feedback effects between similarity and social influence in online communities
D Crandall, D Cosley, D Huttenlocher, J Kleinberg, S Suri
Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008
Inferring social ties from geographic coincidences
DJ Crandall, L Backstrom, D Cosley, S Suri, D Huttenlocher, J Kleinberg
Proceedings of the National Academy of Sciences 107 (52), 22436-22441, 2010
Ego4d: Around the world in 3,000 hours of egocentric video
K Grauman, A Westbury, E Byrne, Z Chavis, A Furnari, R Girdhar, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Diverse beam search: Decoding diverse solutions from neural sequence models
AK Vijayakumar, M Cogswell, RR Selvaraju, Q Sun, S Lee, D Crandall, ...
arXiv preprint arXiv:1610.02424, 2016
Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions
S Bambach, S Lee, DJ Crandall, C Yu
Proceedings of the IEEE international conference on computer vision, 1949-1957, 2015
Discrete-continuous optimization for large-scale structure from motion
D Crandall, A Owens, N Snavely, D Huttenlocher
CVPR 2011, 3001-3008, 2011
Spatial priors for part-based recognition using statistical models.
D Crandall, P Felzenszwalb, D Huttenlocher
IEEE Conference on Computer Vision and Pattern Recognition, 2005
Landmark classification in large-scale image collections
Y Li, DJ Crandall, DP Huttenlocher
2009 IEEE 12th international conference on computer vision, 1957-1964, 2009
Discovering Localized Attributes for Fine-grained Recognition
K Duan, D Parikh, D Crandall, K Grauman
CVPR, 2012
Dynamic dual-attentive aggregation learning for visible-infrared person re-identification
M Ye, J Shen, D J. Crandall, L Shao, J Luo
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
Why m heads are better than one: Training a diverse ensemble of deep networks
S Lee, S Purushwalkam, M Cogswell, D Crandall, D Batra
arXiv preprint arXiv:1511.06314, 2015
Zero-shot video object segmentation via attentive graph neural networks
W Wang, X Lu, J Shen, DJ Crandall, L Shao
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
Weakly supervised learning of part-based spatial models for visual object recognition
DJ Crandall, DP Huttenlocher
Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz …, 2006
Privacy behaviors of lifeloggers using wearable cameras
R Hoyle, R Templeman, S Armes, D Anthony, D Crandall, A Kapadia
Proceedings of the 2014 ACM international joint conference on pervasive and …, 2014
Diverse beam search for improved description of complex scenes
A Vijayakumar, M Cogswell, R Selvaraju, Q Sun, S Lee, D Crandall, ...
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
Hope-net: A graph-based model for hand-object pose estimation
B Doosti, S Naha, M Mirbagheri, DJ Crandall
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
Real-time, cloud-based object detection for unmanned aerial vehicles
J Lee, J Wang, D Crandall, S Šabanović, G Fox
2017 First IEEE International Conference on Robotic Computing (IRC), 36-43, 2017
Stochastic multiple choice learning for training diverse deep ensembles
S Lee, S Purushwalkam Shiva Prakash, M Cogswell, V Ranjan, ...
Advances in Neural Information Processing Systems 29, 2016
SfM with MRFs: Discrete-continuous optimization for large-scale structure from motion
DJ Crandall, A Owens, N Snavely, DP Huttenlocher
IEEE transactions on pattern analysis and machine intelligence 35 (12), 2841 …, 2012
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Artículos 1–20