PoE-Fuse: Precision-Weighted Expert Fusion for Bi-Temporal Change Understanding
Bi-temporal change understanding, which localizes and characterizes what changed between two satellite images, is central to disaster response and environmental monitoring, spanning change detection, building localization, and damage assessment. Strong vision-language models address these tasks, but adapting them typically requires full fine-tuning or reinforcement learning, which is costly and unstable. We propose PoE-Fuse, a parameter-efficient framework that instead composes frozen foundation experts for geometry, grounding, and language, resampling their features onto a shared spatial grid and training only a lightweight fusion trunk. PoE-Fuse treats the aligned features as Gaussian o...
| Authors | Haruki Watase, Shunya Nagashima, Takayuki Nishimura |
|---|---|
| Primary category | cs.CV |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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