AI RESEARCH
Rank-Aware Quantile Activation for Motion-Robust Crop Segmentation in UAV Imagery
arXiv CS.CV
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ArXi:2606.01118v1 Announce Type: new 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.