TeachAugment: Data Augmentation Optimization Using Teacher Knowledge T Suzuki Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 57 | 2022 |
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 | 43 | 2018 |
Superpixel Segmentation Via Convolutional Neural Networks with Regularized Information Maximization T Suzuki ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 27 | 2020 |
Changing fashion cultures K Abe*, T Suzuki*, S Ueta, A Nakamura, Y Satoh, H Kataoka, ... arXiv preprint arXiv:1703.07920, 2017 | 15 | 2017 |
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 | 13 | 2022 |
Adversarial Transformations for Semi-Supervised Learning T Suzuki, I Sato Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5916-5923, 2020 | 13 | 2020 |
Clustering as attention: Unified image segmentation with hierarchical clustering T Suzuki arXiv preprint arXiv:2205.09949, 2022 | 12* | 2022 |
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 | 12 | 2017 |
Superpixel convolution for segmentation T Suzuki, S Akizuki, N Kato, Y Aoki 2018 25th IEEE International Conference on Image Processing (ICIP), 3249-3253, 2018 | 11 | 2018 |
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 | 8 | 2018 |
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields T Suzuki arXiv preprint arXiv:2309.06030, 2023 | 6 | 2023 |
Tabulated MLP for Fast Point Feature Embedding Y Sekikawa, T Suzuki arXiv preprint arXiv:1912.00790, 2019 | 6 | 2019 |
Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning T Suzuki arXiv preprint arXiv:2403.11460, 2024 | 5 | 2024 |
Rethinking PointNet Embedding for Faster and Compact Model T Suzuki, K Ozawa, Y Sekikawa 2020 International Conference on 3D Vision (3DV), 791-800, 2020 | 5 | 2020 |
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 | 5 | 2020 |
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 | 4 | 2020 |
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 | 3 | 2021 |
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 | 3 | 2017 |
Graph-text contrastive learning of inorganic crystal structure toward a foundation model of inorganic materials K Ozawa, T Suzuki, S Tonogai, T Itakura Science and Technology of Advanced Materials: Methods, 2406219, 2024 | 2 | 2024 |
Multi-task Curriculum Learning Based on Gradient Similarity H Igarashi, K Yoneji, K Ishikawa, R Kawakami, T Suzuki, S Yashima, ... The British Machine Vision Conference (BMVC), 2022 | 2 | 2022 |