Tomas Pfister
Tomas Pfister
Head of AI Research @ Google Cloud
Verified email at - Homepage
Cited by
Cited by
Learning from simulated and unsupervised images through adversarial training
A Shrivastava, T Pfister, O Tuzel, J Susskind, W Wang, R Webb
Proceedings of the IEEE conference on computer vision and pattern …, 2017
Temporal fusion transformers for interpretable multi-horizon time series forecasting
B Lim, SÖ Arık, N Loeff, T Pfister
International Journal of Forecasting 37 (4), 1748-1764, 2021
Tabnet: Attentive interpretable tabular learning
SÖ Arik, T Pfister
Proceedings of the AAAI conference on artificial intelligence 35 (8), 6679-6687, 2021
Cutpaste: Self-supervised learning for anomaly detection and localization
CL Li, K Sohn, J Yoon, T Pfister
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
Flowing convnets for human pose estimation in videos
T Pfister, J Charles, A Zisserman
Proceedings of the IEEE international conference on computer vision, 1913-1921, 2015
A Spontaneous Micro-expression Database: Inducement, Collection and Baseline
X Li, T Pfister, X Huang, G Zhao, M Pietikäinen
Automatic Face and Gesture Recognition (FG), 2013
Recognising Spontaneous Facial Micro-expressions
T Pfister, X Li, G Zhao, M Pietikäinen
International Conference on Computer Vision (ICCV), 2011
Learning to prompt for continual learning
Z Wang, Z Zhang, CY Lee, H Zhang, R Sun, X Ren, G Su, V Perot, J Dy, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022
A Simple Semi-Supervised Learning Framework for Object Detection
K Sohn, Z Zhang, CL Li, H Zhang, CY Lee, T Pfister
arXiv preprint arXiv:2005.04757, 2020
Towards reading hidden emotions: A comparative study of spontaneous micro-expression spotting and recognition methods
X Li, X Hong, A Moilanen, X Huang, T Pfister, G Zhao, M Pietikäinen
IEEE transactions on affective computing 9 (4), 563-577, 2017
On completeness-aware concept-based explanations in deep neural networks
CK Yeh, B Kim, S Arik, CL Li, T Pfister, P Ravikumar
Advances in neural information processing systems 33, 20554-20565, 2020
Pseudoseg: Designing pseudo labels for semantic segmentation
Y Zou, Z Zhang, H Zhang, CL Li, X Bian, JB Huang, T Pfister
arXiv preprint arXiv:2010.09713, 2020
Dualprompt: Complementary prompting for rehearsal-free continual learning
Z Wang, Z Zhang, S Ebrahimi, R Sun, H Zhang, CY Lee, X Ren, G Su, ...
European Conference on Computer Vision, 631-648, 2022
Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes
CY Hsieh, CL Li, CK Yeh, H Nakhost, Y Fujii, A Ratner, R Krishna, CY Lee, ...
arXiv preprint arXiv:2305.02301, 2023
Learning and evaluating representations for deep one-class classification
K Sohn, CL Li, J Yoon, M Jin, T Pfister
arXiv preprint arXiv:2011.02578, 2020
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ...
Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022
Deep Convolutional Neural Networks for Efficient Pose Estimation in Gesture Videos
T Pfister, K Simonyan, J Charles, A Zisserman
Asian Conference on Computer Vision (ACCV), 2014
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost
M Gao, Z Zhang, G Yu, SO Arik, LS Davis, T Pfister
ECCV, 2020
Data Valuation using Reinforcement Learning
J Yoon, SO Arik, T Pfister
ICML, 2020
Distilling effective supervision from severe label noise
Z Zhang, H Zhang, SO Arik, H Lee, T Pfister
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
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