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Judy Hoffman
Judy Hoffman
Assistant Professor, Georgia Tech
Zweryfikowany adres z gatech.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
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Decaf: A deep convolutional activation feature for generic visual recognition
J Donahue, Y Jia, O Vinyals, J Hoffman, N Zhang, E Tzeng, T Darrell
International Conference on Machine Learning (ICML), 2013
61632013
Adversarial discriminative domain adaptation
E Tzeng, J Hoffman, K Saenko, T Darrell
Proceedings of the IEEE conference on computer vision and pattern …, 2017
57912017
Cycada: Cycle-consistent adversarial domain adaptation
J Hoffman, E Tzeng, T Park, JY Zhu, P Isola, K Saenko, AA Efros, T Darrell
ICML, 2018
35572018
Deep domain confusion: Maximizing for domain invariance
E Tzeng, J Hoffman, N Zhang, K Saenko, T Darrell
arXiv preprint arXiv:1412.3474, 2014
33062014
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
16312015
Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
J Hoffman, D Wang, F Yu, T Darrell
arXiv preprint arXiv:1612.02649, 2016
9092016
Visda: The visual domain adaptation challenge
X Peng, B Usman, N Kaushik, J Hoffman, D Wang, K Saenko
arXiv preprint arXiv:1710.06924, 2017
8932017
Inferring and executing programs for visual reasoning
J Johnson, B Hariharan, L Van Der Maaten, J Hoffman, L Fei-Fei, ...
Proceedings of the IEEE international conference on computer vision, 2989-2998, 2017
6502017
Cross Modal Distillation for Supervision Transfer
S Gupta, J Hoffman, J Malik
Computer Vision and Pattern Recognition (CVPR), 2016
6412016
LSDA: Large scale detection through adaptation
J Hoffman, S Guadarrama, ES Tzeng, R Hu, J Donahue, R Girshick, ...
Advances in neural information processing systems 27, 2014
3862014
Efficient learning of domain-invariant image representations
J Hoffman, E Rodner, J Donahue, T Darrell, K Saenko
International Conference on Learning Representations (ICLR), 2013
3642013
Label efficient learning of transferable representations acrosss domains and tasks
Z Luo, Y Zou, J Hoffman, LF Fei-Fei
Advances in neural information processing systems 30, 2017
3452017
Token Merging: Your ViT But Faster
D Bolya, CY Fu, X Dai, P Zhang, C Feichtenhofer, J Hoffman
International Conference on Learning Representations (ICLR), 2023, 2022
3252022
Predictive inequity in object detection
B Wilson, J Hoffman, J Morgenstern
arXiv preprint arXiv:1902.11097, 2019
3072019
Learning with side information through modality hallucination
J Hoffman, S Gupta, T Darrell
Proceedings of the IEEE conference on computer vision and pattern …, 2016
2772016
Clockwork convnets for video semantic segmentation
E Shelhamer, K Rakelly, J Hoffman, T Darrell
Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016
2672016
Algorithms and theory for multiple-source adaptation
J Hoffman, M Mohri, N Zhang
Advances in neural information processing systems 31, 2018
2652018
Discovering latent domains for multisource domain adaptation
J Hoffman, B Kulis, T Darrell, K Saenko
Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012
2282012
Learning to balance specificity and invariance for in and out of domain generalization
P Chattopadhyay, Y Balaji, J Hoffman
ECCV, 2020
2202020
TIDE: A General Toolbox for Identifying Object Detection Errors
D Bolya, S Foley, J Hays, J Hoffman
ECCV, 2020
2162020
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