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Teppei Suzuki
Teppei Suzuki
Denso IT Laboratory, Inc.
Verified email at d-itlab.co.jp
Title
Cited by
Cited by
Year
Drive video analysis for the detection of traffic near-miss incidents
H Kataoka, T Suzuki, S Oikawa, Y Matsui, Y Satoh
2018 IEEE International Conference on Robotics and Automation (ICRA), 3421-3428, 2018
382018
TeachAugment: Data Augmentation Optimization Using Teacher Knowledge
T Suzuki
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
342022
Superpixel Segmentation Via Convolutional Neural Networks with Regularized Information Maximization
T Suzuki
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
212020
Changing fashion cultures
K Abe*, T Suzuki*, S Ueta, A Nakamura, Y Satoh, H Kataoka, ...
arXiv preprint arXiv:1703.07920, 2017
152017
Adversarial Transformations for Semi-Supervised Learning
T Suzuki, I Sato
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5916-5923, 2020
132020
Pedestrian near-miss analysis on vehicle-mounted driving recorders
T Suzuki, Y Aoki, H Kataoka
2017 Fifteenth IAPR International Conference on Machine Vision Applications …, 2017
122017
Feature Space Particle Inference for Neural Network Ensembles
S Yashima, T Suzuki, K Ishikawa, I Sato, R Kawakami
Proceedings of the 39th International Conference on Machine Learning, 2022
112022
Superpixel convolution for segmentation
T Suzuki, S Akizuki, N Kato, Y Aoki
2018 25th IEEE International Conference on Image Processing (ICIP), 3249-3253, 2018
102018
Clustering as attention: Unified image segmentation with hierarchical clustering
T Suzuki
arXiv preprint arXiv:2205.09949, 2022
7*2022
Fashion Culture Database: Construction of Database for World-wide Fashion Analysis
K Abe, M Minoguchi, T Suzuki, T Suzuki, N Akimoto, Y Qiu, R Suzuki, ...
2018 15th International Conference on Control, Automation, Robotics and …, 2018
72018
Tabulated MLP for Fast Point Feature Embedding
Y Sekikawa, T Suzuki
arXiv preprint arXiv:1912.00790, 2019
52019
Joint Pedestrian Detection and Risk-level Prediction with Motion-Representation-by-Detection
H Kataoka, T Suzuki, K Nakashima, Y Satoh, Y Aoki
2020 IEEE International Conference on Robotics and Automation (ICRA), 1021-1027, 2020
42020
Rethinking PointNet Embedding for Faster and Compact Model
T Suzuki, K Ozawa, Y Sekikawa
2020 International Conference on 3D Vision (3DV), 791-800, 2020
42020
QR-code Reconstruction from Event Data via Optimization in Code Subspace
J Nagata, Y Sekikawa, K Hara, T Suzuki, Y Aoki
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020
42020
Does End-to-End Trained Deep Model Always Perform Better than Non-End-to-End Counterpart?
I Sato, G Liu, K Ishikawa, T Suzuki, M Tanaka
Electronic Imaging 2021 (10), 240-1-240-7, 2021
32021
Smooth Transfer Learning for Source-to-Target Generalization
K Takayama, I Sato, T Suzuki, R Kawakami, K Uto, K Shinoda
NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021
22021
cvpaper. challenge in 2016: futuristic computer vision through 1,600 papers survey
H Kataoka, S Shirakabe, Y He, S Ueta, T Suzuki, K Abe, A Kanezaki, ...
arXiv preprint arXiv:1707.06436, 2017
22017
Tracking People in Dense Crowds Using Supervoxels
S Takayama, T Suzuki, Y Aoki, S Isobe, M Masuda
2016 12th International Conference on Signal-Image Technology & Internet …, 2016
22016
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields
T Suzuki
arXiv preprint arXiv:2309.06030, 2023
12023
Irregularly Tabulated MLP for Fast Point Feature Embedding
Y Sekikawa, T Suzuki
arXiv preprint arXiv:2011.09852, 2020
12020
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Articles 1–20