Rank-Aware Quantile Activation for Motion-Robust Crop Segmentation in UAV Imagery
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913176973475840 |
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| author | Kiran, Abinav Danda, Sravan Challa, Aditya Sen, Sougata S, Daya Sagar B |
| author_facet | Kiran, Abinav Danda, Sravan Challa, Aditya Sen, Sougata S, Daya Sagar B |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_01118 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rank-Aware Quantile Activation for Motion-Robust Crop Segmentation in UAV Imagery Kiran, Abinav Danda, Sravan Challa, Aditya Sen, Sougata S, Daya Sagar B Computer Vision and Pattern Recognition 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. |
| title | Rank-Aware Quantile Activation for Motion-Robust Crop Segmentation in UAV Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2606.01118 |