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arXiv Computer Vision By Xingyan Li, Jordan A. Caraballo-Vega, Jie Gong, Mark L. Carroll, Jianwu Wang

SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models

Read the original on arXiv Computer Vision →

SPECTRA is a parameter‑efficient fine‑tuning framework for geospatial foundation models that tackles two key challenges: spectral mismatch and high adaptation cost. It introduces Band‑Routed Embedding (BRE) to map downstream sensor bands into the pretrained model’s expected band space, enabling full use of available spectral data without altering the patch embedding interface. Additionally, Stage‑wise Transferability‑aware LoRA (ST‑LoRA) estimates stage‑wise transferability and assigns LoRA ranks accordingly, concentrating trainable parameters on the most transferable stages and reducing overall adaptation cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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