Research Community
Projects
Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves,
Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota,
CVPR 2023.
[
Paper]
[
Project]
[
Code]
Pre-training Auto-generated Volumetric Shapes for 3D Medical Image Segmentation,
Ryu Tadokoro, Ryosuke Yamada, Hirokatsu Kataoka,
CVPR 2023 Workshop.
Point Cloud Pre-training with Natural 3D Structures,
Ryosuke Yamada, Hirokatsu Kataoka, Naoya Chiba, Yukiyasu Domae, Tetsuya Ogata,
CVPR 2022.
[
Paper]
[
Project]
[
Code]
Replacing Labeled Real-Image Datasets with Auto-Generated Contours,
Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota,
CVPR 2022.
[
Paper]
[
Project]
[
Code]
[
Oral]
[
Poster]
Pre-training without Natural Images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh,
IJCV 2022.
[
Paper]
[
Project]
[
Code]
Can Vision Transformers Learn without Natural Images?
Kodai Nakashima, Hirokatsu Kataoka, Asato Matsumoto, Kenji Iwata, Nakamasa Inoue,
AAAI 2022.
[
Paper]
[
Project]
[
Code]
[
Dataset]
Spatiotemporal Initialization for 3D CNNs with Generated Motion Patterns
Hirokatsu Kataoka, Eisuke Yamagata, Kensho Hara, Ryusuke Hayashi, Nakamasa Inoue,
WACV 2022.
[
Paper]
[
Project]
Formula-driven Supervised Learning with Recursive Tiling Patterns
Hirokatsu Kataoka, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Yutaka Satoh,
ICCV 2021 Workshop.
[
Paper]
[
Project]
MV-FractalDB: Formula-driven Supervised Learning for Multi-view Image Recognition
Ryosuke Yamada, Ryo Takahashi, Ryota Suzuki, Akio Nakamura, Yusuke Yoshiyasu, Ryusuke Sagawa, Hirokatsu Kataoka,
IROS 2021.
[
Paper]
[
Project]
Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data
Nakamasa Inoue, Eisuke Yamagata, Hirokatsu Kataoka,
ICPR 2020.
[
Paper]
[
YouTube]
Pre-training without Natural Images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh,
ACCV 2020 Best Paper Honorable Mention Award.
[
Paper]
[
Project]
[
Code]
[
Oral]
[
Poster]
[
Supp. Mat.]
Invited Talks
Pre-training without Natural Images
Hirokatsu Kataoka
IW-FCV 2023
[
Link]
[
Slide]
Pre-training without Natural Images
Hirokatsu Kataoka
MIT
[
Slide]
自然法則に基づく深層学習
Hirokatsu Kataoka
NVIDIA HPC Week - HPC + Machine Learning
[
Link]
[
Slide]
限られたデータからの深層学習
Nakamasa Inoue
MIRU 2021
[
Link] [
Slide]
Pre-training without Natural Images
Hirokatsu Kataoka
SNL 2021
[
Link]
[
Slide]
Group
Acknowledgement
- This work is based on results obtained from a project, JPNP20006, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
- This work is supported by JSPS KAKENHI Grant Number JP19H01134.
- Computational resource of AI Bridging Cloud Infrastructure (ABCI) provided by National Institute of Advanced Industrial Science and Technology (AIST) is used.
- The page template is the courtesy of GenForce.