1 citations · 1 across the 6 of their papers we have counts for
9 papers
FORUM: Frozen Outputs Reconciled Using Model Agreement for Visual Grounding
Taiyo Sato, Takamasa Sanda, Keisuke Maeda +3
Frozen multimodal large language models (MLLMs) now solve standard referring expression comprehension with a single prompted call, yet on adversarial benchmarks with same-category…
PoE-Fuse: Precision-Weighted Expert Fusion for Bi-Temporal Change Understanding
Haruki Watase, Shunya Nagashima, Takayuki Nishimura
Bi-temporal change understanding, which localizes and characterizes what changed between two satellite images, is central to disaster response and environmental monitoring, spannin…
Forecast Workflow Bench: Evaluating Language-Model Decisions with Budgeted Forecast Tools
Shunya Nagashima
Time-series foundation models (TSFMs) provide forecasts for operational decisions, but accuracy alone does not determine their value. Evaluating agents that use these models requir…
Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura
Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation…
FLARE-SSM: Deep State Space Models with Influence-Balanced Loss for 72-Hour Solar Flare Prediction
Yusuke Takagi, Shunya Nagashima, Komei Sugiura
Accurate and reliable solar flare predictions are essential to mitigate potential impacts on critical infrastructure. However, the current performance of solar flare forecasting is…
Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images
Shunya Nagashima, Komei Sugiura
Accurate, reliable solar flare prediction is crucial for mitigating potential disruptions to critical infrastructure, while predicting solar flares remains a significant challenge.…
DM2RM: Dual-Mode Multimodal Ranking for Target Objects and Receptacles Based on Open-Vocabulary Instructions
Ryosuke Korekata, Kanta Kaneda, Shunya Nagashima +2
In this study, we aim to develop a domestic service robot (DSR) that, guided by open-vocabulary instructions, can carry everyday objects to the specified pieces of furniture. Few e…
Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine
Kanta Kaneda, Shunya Nagashima, Ryosuke Korekata +2
Domestic service robots offer a solution to the increasing demand for daily care and support. A human-in-the-loop approach that combines automation and operator intervention is con…
DialMAT: Dialogue-Enabled Transformer with Moment-Based Adversarial Training
Kanta Kaneda, Ryosuke Korekata, Yuiga Wada +7
This paper focuses on the DialFRED task, which is the task of embodied instruction following in a setting where an agent can actively ask questions about the task. To address this…