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arXiv Computer Vision By Tianle Fang, Zhenbing Liu, Chong Yin, Bolun Li, Haoxiang Lu

C2FXNet: Coarse-to-Fine Scene Expert for Unified Object Detection across Adverse Weather

Read the original on arXiv Computer Vision →

C2FXNet is a unified object detection framework designed for adverse weather conditions. It uses a dual-level guidance mechanism: a Multi-step Reasoning Router (MRR) for coarse scene reasoning and a Fine Scene Refinement (FSR) module for fine-grained semantic adjustment. A Scene-aware Mixture-of-Experts (SMoE) dynamically combines scene-specific experts, enabling robust detection across foggy, dark, and clear scenes without scene-specific training.

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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