{"ID":2831913,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.11874","arxiv_id":"2512.11874","title":"Pseudo-Label Refinement for Robust Wheat Head Segmentation via Two-Stage Hybrid Training","abstract":"This extended abstract details our solution for the Global Wheat Full Semantic Segmentation Competition. We developed a systematic self-training framework. This framework combines a two-stage hybrid training strategy with extensive data augmentation. Our core model is SegFormer with a Mix Transformer (MiT-B4) backbone. We employ an iterative teacher-student loop. This loop progressively refines model accuracy. It also maximizes data utilization. Our method achieved competitive performance. This was evident on both the Development and Testing Phase datasets.","short_abstract":"This extended abstract details our solution for the Global Wheat Full Semantic Segmentation Competition. We developed a systematic self-training framework. This framework combines a two-stage hybrid training strategy with extensive data augmentation. Our core model is SegFormer with a Mix Transformer (MiT-B4) backbone....","url_abs":"https://arxiv.org/abs/2512.11874","url_pdf":"https://arxiv.org/pdf/2512.11874v1","authors":"[\"Jiahao Jiang\",\"Zhangrui Yang\",\"Xuanhan Wang\",\"Jingkuan Song\"]","published":"2025-12-07T02:48:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false}