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arXiv Machine Learning By Liuyin Shi, Chengqi Li, Wenbo He

Shifting Focus by Reliability: Geometric Fusion Modulation for Robust RGB-T Detection

Read the original on arXiv Machine Learning →

The paper introduces PG‑FM, a Prompt‑Guided Feature Modulation method that adapts RGB‑thermal object detectors to the estimated corruption type and severity of each modality. Unlike restoration‑based approaches, PG‑FM performs per‑input adaptation without significant latency and maintains clean‑set performance while boosting mAP50 by up to 20.8 points on FLIR and 19.9 points on KAIST under simulated corruptions. The method also transfers effectively to unmodified, naturally acquired external RGB‑T datasets without retraining.

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 Machine Learning.

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