David Crandall

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102 artykuły

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Chen YuProfessor, The University of Texas at AustinZweryfikowany adres z austin.utexas.edu
Apu KapadiaProfessor of Computer Science, Indiana University BloomingtonZweryfikowany adres z indiana.edu
Daniel HuttenlocherMassachusetts Institute of TechnologyZweryfikowany adres z mit.edu
Mingze XuAppleZweryfikowany adres z apple.com
Stefan LeeAssistant Professor, Oregon State UniversityZweryfikowany adres z oregonstate.edu
Geoffrey FoxProfessor of Computer Science, University of Virginia, Biocomplexity InstituteZweryfikowany adres z virginia.edu
Sven BambachData Science & Machine Learning at Nationwide Children's HospitalZweryfikowany adres z nationwidechildrens.org
Dhruv BatraPrev: FAIR (Meta AI), Georgia TechZweryfikowany adres z dhruvbatra.com
Rob TemplemanExecutive Director for Cyber Innovation, Indiana UniversityZweryfikowany adres z iu.edu
Mohammed KorayemDirector of Data Science, Data Science R&D - CareerBuilderZweryfikowany adres z indiana.edu
John PadenAssoc. Scientist/Assoc. Director, University of KansasZweryfikowany adres z ku.edu
Wenguan WangZhejiang UniversityZweryfikowany adres z zju.edu.cn
Md Alimoor RezaAssistant Professor, Drake UniversityZweryfikowany adres z drake.edu
Jiebo Luo, Fellow of ACM/AAAI/IEEE/...Albert Arendt Hopeman Professor of Engineering, University of RochesterZweryfikowany adres z cs.rochester.edu
Roberto HoyleAssistant Professor, Oberlin CollegeZweryfikowany adres z oberlin.edu
Michael CogswellSRI InternationalZweryfikowany adres z sri.com
Satoshi TsutsuiNanyang Technological UniversityZweryfikowany adres z indiana.edu
David LeakeProfessor of Computer Science, Indiana UniversityZweryfikowany adres z iu.edu
Selma ŠabanovićProfessor of Informatics, Indiana University BloomingtonZweryfikowany adres z iu.edu
Jon KleinbergProfessor of Computer Science, Cornell UniversityZweryfikowany adres z cs.cornell.edu

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David Crandall

Professor of Computer Science, Indiana University

Zweryfikowany adres z indiana.edu - Strona główna

Computer vision machine learning data mining social networks


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
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	1128	2009
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	992	2022
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	885	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	622	2010
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	604	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	534	2015
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	481	2020
Discrete-continuous optimization for large-scale structure from motion D Crandall, A Owens, N Snavely, D Huttenlocher CVPR 2011, 3001-3008, 2011	468	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	435	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	379	2009
Discovering Localized Attributes for Fine-grained Recognition K Duan, D Parikh, D Crandall, K Grauman CVPR, 2012	360	2012
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	357	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	332	2019
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	268	2018
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	266	2006
A survey on deep learning technique for video segmentation T Zhou, F Porikli, DJ Crandall, L Van Gool, W Wang IEEE transactions on pattern analysis and machine intelligence 45 (6), 7099-7122, 2022	257*	2022
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	247	2020
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	247	2014
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	225	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	222	2016

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