1 code implementation • NeurIPS 2023 • Taiki Miyanishi, Fumiya Kitamori, Shuhei Kurita, Jungdae Lee, Motoaki Kawanabe, Nakamasa Inoue
To tackle this problem, we introduce the CityRefer dataset for city-level visual grounding.
1 code implementation • 2 Jun 2022 • Reinmar J Kobler, Jun-Ichiro Hirayama, Qibin Zhao, Motoaki Kawanabe
To achieve this, we propose a new building block for geometric deep learning, which we denote SPD domain-specific momentum batch normalization (SPDDSMBN).
1 code implementation • CVPR 2022 • Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, Motoaki Kawanabe
We propose a new 3D spatial understanding task of 3D Question Answering (3D-QA).
1 code implementation • 30 Jul 2021 • Reinmar J. Kobler, Jun-Ichiro Hirayama, Lea Hehenberger Catarina Lopes-Dias, Gernot R. Müller-Putz, Motoaki Kawanabe
Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development.
no code implementations • ICML 2017 • Jun-Ichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe
We present a novel probabilistic framework for a hierarchical extension of independent component analysis (ICA), with a particular motivation in neuroscientific data analysis and modeling.
no code implementations • NeurIPS 2013 • Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, Motoaki Kawanabe
The efficiency of Brain-Computer Interfaces (BCI) largely depends upon a reliable extraction of informative features from the high-dimensional EEG signal.
no code implementations • 6 Dec 2009 • David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Mueller
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data.
no code implementations • NeurIPS 2007 • Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V. Buenau, Motoaki Kawanabe
In this paper, we propose a direct importance estimation method that does not require the input density estimates.