LER-YOLO reports 89.7% AP50 on the MBU benchmark for misaligned RGB-IR UAV detection by routing among RGB-dominant, IR-dominant, and fusion experts using a spatial reliability map.
MoE3D: Mixture of Experts Meets Multi-Modal 3D Understanding.arXiv 2025
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A routing framework maintains three parallel 3D feature streams for LiDAR, 4D radar, and fusion, with a lightweight router using weather prompts to dynamically weight them and auxiliary supervision to keep branches distinct, achieving SOTA on K-Radar.
citing papers explorer
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LER-YOLO: Reliability-Aware Expert Routing for Misaligned RGB-Infrared UAV Detection
LER-YOLO reports 89.7% AP50 on the MBU benchmark for misaligned RGB-IR UAV detection by routing among RGB-dominant, IR-dominant, and fusion experts using a spatial reliability map.
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Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection
A routing framework maintains three parallel 3D feature streams for LiDAR, 4D radar, and fusion, with a lightweight router using weather prompts to dynamically weight them and auxiliary supervision to keep branches distinct, achieving SOTA on K-Radar.