Παρακολούθηση
Kate Saenko
Kate Saenko
Boston University
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα bu.edu - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
Long-term recurrent convolutional networks for visual recognition and description
J Donahue, L Anne Hendricks, S Guadarrama, M Rohrbach, ...
Proceedings of the IEEE conference on computer vision and pattern …, 2015
79692015
Adversarial discriminative domain adaptation
E Tzeng, J Hoffman, K Saenko, T Darrell
Computer Vision and Pattern Recognition (CVPR), 2017
57852017
Deep coral: Correlation alignment for deep domain adaptation
B Sun, K Saenko
Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016
36282016
Cycada: Cycle-consistent adversarial domain adaptation
J Hoffman, E Tzeng, T Park, JY Zhu, P Isola, K Saenko, AA Efros, T Darrell
International Conference on Machine Learning 2018, 2018
35652018
Adapting visual category models to new domains
K Saenko, B Kulis, M Fritz, T Darrell
Computer Vision–ECCV 2010: 11th European Conference on Computer Vision …, 2010
34982010
Deep domain confusion: Maximizing for domain invariance
E Tzeng, J Hoffman, N Zhang, K Saenko, T Darrell
arXiv preprint arXiv:1412.3474, 2014
33082014
Return of Frustratingly Easy Domain Adaptation
B Sun, J Feng, K Saenko
AAAI, 2016
21962016
Moment matching for multi-source domain adaptation
X Peng, Q Bai, X Xia, Z Huang, K Saenko, B Wang
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
20512019
Sequence to sequence-video to text
S Venugopalan, M Rohrbach, J Donahue, R Mooney, T Darrell, K Saenko
Proceedings of the IEEE international conference on computer vision, 4534-4542, 2015
18102015
Simultaneous deep transfer across domains and tasks
E Tzeng, J Hoffman, T Darrell, K Saenko
Proceedings of the IEEE international conference on computer vision, 4068-4076, 2015
16332015
Rise: Randomized Input Sampling for Explanation of black-box models
V Petsiuk
arXiv preprint arXiv:1806.07421, 2018
13642018
Translating videos to natural language using deep recurrent neural networks
S Venugopalan, H Xu, J Donahue, M Rohrbach, R Mooney, K Saenko
NAACL HLT 2015, 2014
12432014
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
H Xu, K Saenko
European Conference on Computer Vision (ECCV), 2016
9452016
R-C3D: Region convolutional 3d network for temporal activity detection
H Xu, A Das, K Saenko
International Conference on Computer Vision (ICCV), 2017
9102017
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
B Kulis, K Saenko, T Darrell
CVPR 2011, 1785-1792, 2011
9042011
Visda: The visual domain adaptation challenge
X Peng, B Usman, N Kaushik, J Hoffman, D Wang, K Saenko
arXiv preprint arXiv:1710.06924, 2017
8892017
Strong-weak distribution alignment for adaptive object detection
K Saito, Y Ushiku, T Harada, K Saenko
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
7872019
Semi-supervised domain adaptation via minimax entropy
K Saito, D Kim, S Sclaroff, T Darrell, K Saenko
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
7772019
Learning to reason: End-to-end module networks for visual question answering
R Hu, J Andreas, M Rohrbach, T Darrell, K Saenko
International Conference on Computer Vision (ICCV), 2017
7162017
Natural language object retrieval
R Hu, H Xu, M Rohrbach, J Feng, K Saenko, T Darrell
Proceedings of the IEEE conference on computer vision and pattern …, 2016
6502016
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