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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…
Physics-Guided Flow-Map Matching for Precipitation Nowcasting
Shunya Nagashima, Takumi Bannai, Makoto Misaizu +3
Precipitation nowcasting, generating future radar fields from past observations, is critical for flood warning and disaster response. It is also a demanding benchmark for spatiotem…
Impact of Expert-Following Strategies in Financial Asset Recommendation
Ryuki Unno, Koshi Watanabe, Keigo Sakurai +3
Financial institutions hold rich transaction histories, yet delivering recommendations that simultaneously maximize investment returns and ensure preference alignment remains a sig…
Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection
Yuhu Feng, Keisuke Maeda, Takahiro Ogawa +1
The deployment of data-driven computer vision models for structural health monitoring (SHM) is heavily constrained by the data silo dilemma due to stringent privacy and security re…
Predictive but Not Plannable: RC-aux for Latent World Models
Wenyuan Li, Guang Li, Keisuke Maeda +2
A latent world model may achieve accurate short-horizon prediction while still inducing a latent space that is poorly aligned with planning. A key issue is spatiotemporal mismatch:…
Foreground-Aware Dataset Distillation via Dynamic Patch Selection
Longzhen Li, Guang Li, Ren Togo +3
In this paper, we propose a foreground-aware dataset distillation method that enhances patch selection in a content-adaptive manner. With the rising computational cost of training…