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

arXiv:2609.25693 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/PolarisFTL/C2FXNet

Terminology

• Adaptation — 11× in this paper · explained in 1 episode
• Scene-aware Mixture-of-Experts — 10× in this paper
• Multi-step Reasoning Router — 9× in this paper
• Robustness — 9× in this paper · explained in 5 episodes
• Fine Scene Refinement — 8× in this paper
• Generalization — 7× in this paper · explained in 13 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Adaptability — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Mixture of Experts — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Adverse Weather Dataset — 1× in this paper
• Benchmarking — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Later Mixture-of-Experts — 1× in this paper
• Low-Light Frequency Restoration — 1× in this paper
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Mixture-of-Experts (MoE) Models — 1× in this paper · explained in 3 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Spiking neural networks — 1× in this paper · explained in 2 episodes
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Qwen2.5-VL Technical Report
• YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
• VisualBERT: A Simple and Performant Baseline for Vision and Language
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

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