ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation
Reliable decision support in digital agriculture requires not only accurate predictions but also well-calibrated uncertainty estimates, particularly for dense prediction tasks such as semantic segmentation. Ensembles provide strong uncertainty quantification but are computationally and memory demanding, while single-model approximations often sacrifice uncertainty quality. We propose ST-LoRA, a parameter-efficient ensemble that builds diverse members from a single training trajectory by combining Low-Rank Adaptation (LoRA) with snapshot ensembling. All members share a frozen pretrained backbone and differ only in lightweight low-rank adapters, which sharply reduces trainable parameters, c...
| Authors | Mohamed Farag, Genc Hoxha, Yahia Maleki et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 August 2026 |
| Last updated at source | 5 October 2026 |
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