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20232026
most citedDM2RM: Dual-Mode Multimodal Ranking for Target Objects and Receptacles Based on Open-Vocabulary Instructions

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cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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.…

cs.CV2023

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…