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Pseudo-Label Refinement for Robust Wheat Head Segmentation via Two-Stage Hybrid Training

2025-12-07 02:48:50
Jiahao Jiang, Zhangrui Yang, Xuanhan Wang, Jingkuan Song
arXiv_CV
       
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.

Abstract (translated)

这篇扩展摘要详细介绍了我们为全球小麦全语义分割竞赛提出的解决方案。我们开发了一个系统的自我训练框架，该框架结合了两阶段混合训练策略和广泛的数据增强技术。我们的核心模型是采用Mix Transformer（MiT-B4）骨干网络的SegFormer模型。我们采用了迭代的教师-学生循环机制，这一机制逐步提高模型的准确性，并最大化数据利用率。我们的方法在开发和测试阶段的数据集上均取得了具有竞争力的表现。

URL

https://arxiv.org/abs/2512.11874

PDF

https://arxiv.org/pdf/2512.11874.pdf

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