YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection
This paper presents a novel Mixture-of-Experts framework for object detection, incorporating adaptive routing among multiple YOLOv9-T experts to enable dynamic feature specialization and achieve higher mean Average Precision (mAP) and Average Recall (AR) compared to a single YOLOv9-T model.
692d316c4397b1ec214f694e
Ori Meiraz
692d316c4397b1ec214f694f
Sharon Shalev
692d316c4397b1ec214f6950
Avishai Weizman
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2025-12-02T08:49:40.326Z
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avishai
avishai111
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avishai111
YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection
This paper presents a novel Mixture-of-Experts framework for object detection, incorporating adaptive routing among multiple YOLOv9-T experts to enable dynamic feature specialization and achieve higher mean Average Precision (mAP) and Average Recall (AR) compared to a single YOLOv9-T model.
A Mixture-of-Experts framework with adaptive routing among multiple YOLOv9-T experts improves object detection performance by achieving higher mAP and AR.
Mixture-of-Experts adaptive routing YOLOv9-T dynamic feature specialization mean Average Precision Average Recall