Loss Adaptive Curriculum Learning for Ground-Based Cloud Detection
While deep learning has advanced object detection through hierarchical feature learning and end-to-end optimization, conventional random sampling paradigms exhibit critical limitations in addressing hyperspectral ambiguity and low-distinguishability challenges in ground-based cloud detection. To ove...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2262 |
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| Summary: | While deep learning has advanced object detection through hierarchical feature learning and end-to-end optimization, conventional random sampling paradigms exhibit critical limitations in addressing hyperspectral ambiguity and low-distinguishability challenges in ground-based cloud detection. To overcome these limitations, we propose CurriCloud, a loss-adaptive curriculum framework featuring three key innovations: (1) real-time sample evaluation via Unified Batch Loss (UBL) for difficulty measurement, (2) stabilized training monitoring through a sliding window queue mechanism, and (3) progressive sample selection aligned with model capability using meteorology-guided phase-wise threshold scheduling. Extensive experiments on the ALPACLOUD benchmark demonstrate CurriCloud’s effectiveness across diverse architectures (YOLOv10s, SSD, and RT-DETR-R50), achieving consistent improvements of +3.1% to +11.4% mAP50 over both random sampling baselines and existing curriculum learning methods. |
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| ISSN: | 2072-4292 |