针对无人机影像运动模糊，提出基于排名感知的激活函数提升稀有作物分割精度。
Rank-Aware Quantile Activation for Motion-Robust Crop Segmentation in UAV Imagery
- 用实例级排序归一化替代传统幅度门控，增强对模糊下微弱特征的敏感度。
- 在多种模糊强度下，对稀有结构与纹理类别的分割指标提升显著，最高增益达12.3%。
- 适合高动态无人机农业监测场景，尤其关注稀有作物或细节结构的分割任务。
高速无人机拍摄导致运动模糊，降低对具有高农艺价值但纹理稀少类别的语义分割性能。标准卷积网络依赖易被模糊破坏的高频幅值特征，造成少数类别信号统计湮灭。本文提出双分位数激活（QAct），一种基于排名感知的模块，以实例级排序归一化取代幅度门控。在Agriculture-Vision 2021数据集上，于零样本与模糊监督两种设置下，不同模糊强度均验证了其优越性：相比ReLU，在所有条件下均实现一致的mIoU提升，尤其在稀有结构和纹理依赖类别上表现突出。部分主导类别（如水体、播种遗漏）在知识蒸馏后表现不一。中等模糊下，零样本QAct优于蒸馏训练的ReLU；全模糊强度下，Distill-QAct表现最佳，证实排名感知激活与模糊域训练具有互补的鲁棒性优势。
原文摘要 · Abstract (English)
Motion blur from high-speed UAV acquisition de-grades semantic segmentation on rare texture-dependent classes with high agronomic value. Standard CNNs rely on high-frequency magnitude features that blur destroys, causing statistical erasure of minority signals. We propose Dual Quantile Activation (QAct), a rank-aware block replacing magnitude gating with instance-level rank normalization. Evaluated onAgriculture-Vision 2021 across zero-shot and blur-supervised regimes at multiple severities, QAct is the dominant architectural factor: it delivers consistent mIoU gains over ReLU across both regimes and all severities, with strongest gains on rare structural and texture-dependent classes. Some dominant classes (water,planter skip) show mixed per-class performance under distillation. At moderate blur, zero-shot QAct outperforms distillation-trained ReLU; across all severities, Distill-QAct achieves best performance, confirming rank aware activation and blur-domain training are complementary robustness sources.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。