750 citations · 918 across the 17 of their papers we have counts for
19 papers
SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts
Shanglin Li, Shiwen Chu, Okan Koç +5
Electroencephalography (EEG) based brain-computer interfaces enable direct brain-to-device communication for applications such as rehabilitation and communication. However, their p…
Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG
Shiwen Chu, Shanglin Li, Motoaki Kawanabe +1
Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) often require unsupervised domain adaptation (UDA) to generalize across subjects and sessions. While Riemannian…
NavWAM: A Navigation World Action Model for Goal-Conditioned Visual Navigation
Daichi Azuma, Taiki Miyanishi, Koya Sakamoto +6
Goal-conditioned visual navigation requires a robot to act under partial observability by anticipating how its motion will change the future egocentric view and whether that change…
EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
Runhe Zhou, Shanglin Li, Guanxiang Huang +5
Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, providing great clinical potential…
E3VS-Bench: A Benchmark for Viewpoint-Dependent Active Perception in 3D Gaussian Splatting Scenes
Koya Sakamoto, Taiki Miyanishi, Daichi Azuma +6
Visual search in 3D environments requires embodied agents to actively explore their surroundings and acquire task-relevant evidence. However, existing visual search and embodied AI…
HEEGNet: Hyperbolic Embeddings for EEG
Shanglin Li, Shiwen Chu, Okan Koç +4
Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. Howeve…
An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon
Abhishek Jana, Moeumu Uili, James Atherton +3
This paper presents a largely automated one-shot bird call classification pipeline, incorporating targeted manual quality control steps, designed for rare species absent from large…
SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG
Shanglin Li, Motoaki Kawanabe, Reinmar J. Kobler
The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neuro…
Answerability Fields: Answerable Location Estimation via Diffusion Models
Daichi Azuma, Taiki Miyanishi, Shuhei Kurita +2
In an era characterized by advancements in artificial intelligence and robotics, enabling machines to interact with and understand their environment is a critical research endeavor…
Map-based Modular Approach for Zero-shot Embodied Question Answering
Koya Sakamoto, Daichi Azuma, Taiki Miyanishi +2
Embodied Question Answering (EQA) serves as a benchmark task to evaluate the capability of robots to navigate within novel environments and identify objects in response to human qu…
CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data
Taiki Miyanishi, Fumiya Kitamori, Shuhei Kurita +3
City-scale 3D point cloud is a promising way to express detailed and complicated outdoor structures. It encompasses both the appearance and geometry features of segmented city comp…
Cross3DVG: Cross-Dataset 3D Visual Grounding on Different RGB-D Scans
Taiki Miyanishi, Daichi Azuma, Shuhei Kurita +1
We present a novel task for cross-dataset visual grounding in 3D scenes (Cross3DVG), which overcomes limitations of existing 3D visual grounding models, specifically their restrict…
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Reinmar J Kobler, Jun-ichiro Hirayama, Qibin Zhao +1
Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with millisecond resolution, rendering it a viable method in neuroscience and healthcare. However,…
ScanQA: 3D Question Answering for Spatial Scene Understanding
Daichi Azuma, Taiki Miyanishi, Shuhei Kurita +1
We propose a new 3D spatial understanding task of 3D Question Answering (3D-QA). In the 3D-QA task, models receive visual information from the entire 3D scene of the rich RGB-D ind…
On the interpretation of linear Riemannian tangent space model parameters in M/EEG
Reinmar J. Kobler, Jun-Ichiro Hirayama, Lea Hehenberger Catarina Lopes-Dias +2
Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer inter…
Interpretable brain age prediction using linear latent variable models of functional connectivity
Ricardo Pio Monti, Alex Gibberd, Sandipan Roy +6
Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we…
Insights from Classifying Visual Concepts with Multiple Kernel Learning
Alexander Binder, Shinichi Nakajima, Marius Kloft +5
Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is…
Modeling sparse connectivity between underlying brain sources for EEG/MEG
Stefan Haufe, Ryota Tomioka, Guido Nolte +2
We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of…
How to Explain Individual Classification Decisions
David Baehrens, Timon Schroeter, Stefan Harmeling +3
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. Thus, we get an answer to the…