{"ID":2840593,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13344","arxiv_id":"2511.13344","title":"YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection","abstract":"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.","short_abstract":"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.","url_abs":"https://arxiv.org/abs/2511.13344","url_pdf":"https://arxiv.org/pdf/2511.13344v4","authors":"[\"Ori Meiraz\",\"Sharon Shalev\",\"Avishai Weizman\"]","published":"2025-11-17T13:11:11Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}