1 code implementation • 18 Mar 2024 • Teppei Suzuki
In pursuit of a more scalable 3D reconstruction, we propose a federated learning framework with 3DGS, which is a decentralized framework and can potentially use distributed computational resources across millions of clients.
no code implementations • 12 Sep 2023 • Teppei Suzuki
Therefore, to address these problems, we propose a federated learning pipeline for large-scale modeling with NeRF.
1 code implementation • 2 Jun 2022 • Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami
Ensembles of deep neural networks demonstrate improved performance over single models.
1 code implementation • 20 May 2022 • Teppei Suzuki
Hierarchical clustering is an effective and efficient approach widely used for classical image segmentation methods.
1 code implementation • CVPR 2022 • Teppei Suzuki
Optimization of image transformation functions for the purpose of data augmentation has been intensively studied.
1 code implementation • 5 Mar 2021 • Teppei Suzuki
Superpixels are a useful representation to reduce the complexity of image data.
no code implementations • 13 Nov 2020 • Yusuke Sekikawa, Teppei Suzuki
Aiming at drastic speedup for point-feature embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptrons (MLP) and a lookup table (LUT) to transform point-coordinate inputs into high-dimensional features.
no code implementations • 31 Jul 2020 • Teppei Suzuki, Keisuke Ozawa, Yusuke Sekikawa
PointNet, which is the widely used point-wise embedding method and known as a universal approximator for continuous set functions, can process one million points per second.
1 code implementation • 17 Feb 2020 • Teppei Suzuki
We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time.
no code implementations • 23 Nov 2019 • Yusuke Sekikawa, Teppei Suzuki
Aiming at a drastic speedup for point-data embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptron (MLP) and look-up table (LUT) to transform point-coordinate inputs into high-dimensional features.
no code implementations • 13 Nov 2019 • Teppei Suzuki, Ikuro Sato
We propose a Regularization framework based on Adversarial Transformations (RAT) for semi-supervised learning.
no code implementations • 7 Apr 2018 • Hirokatsu Kataoka, Teppei Suzuki, Shoko Oikawa, Yasuhiro Matsui, Yutaka Satoh
Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely.
2 code implementations • 20 Jul 2017 • Hirokatsu Kataoka, Soma Shirakabe, Yun He, Shunya Ueta, Teppei Suzuki, Kaori Abe, Asako Kanezaki, Shin'ichiro Morita, Toshiyuki Yabe, Yoshihiro Kanehara, Hiroya Yatsuyanagi, Shinya Maruyama, Ryosuke Takasawa, Masataka Fuchida, Yudai Miyashita, Kazushige Okayasu, Yuta Matsuzaki
The paper gives futuristic challenges disscussed in the cvpaper. challenge.
3 code implementations • 23 Mar 2017 • Kaori Abe, Teppei Suzuki, Shunya Ueta, Akio Nakamura, Yutaka Satoh, Hirokatsu Kataoka
The paper presents a novel concept that analyzes and visualizes worldwide fashion trends.
no code implementations • 26 Apr 2016 • Teppei Suzuki, Soma Shirakabe, Yudai Miyashita, Akio Nakamura, Yutaka Satoh, Hirokatsu Kataoka
By the detected change areas, however, a human cannot understand how different the two images.