A Cross-Scale Decoder with Token Refinement for Off-Road Semantic Segmentation

Off-road semantic segmentation is challenging due to irregular terrain, vegetation clutter, class-level similarity, and ambiguous boundary annotations. Existing decoder designs often rely on compact bottlenecks that oversmooth fine structures or repeated multi-scale fusion that can amplify annotation noise and increase computational cost. To address these limitations, we propose a Cross-Scale Decoder for robust off-road semantic segmentation. The proposed decoder first stabilizes semantic representations through Global–Local Token Refinement (GLTR) on a compact bottleneck lattice. It then selectively incorporates fine-scale structural cues using Boundary-Guided Correction (BGC) and Gated Cross-Scale Interaction (GCS), avoiding dense and repeated feature fusion. In addition, uncertainty-guided class-aware point refinement focuses computation on ambiguous and low-confidence regions. Experiments on standard off-road benchmarks demonstrate that the proposed method improves segmentation accuracy and boundary consistency over existing approaches while maintaining practical inference efficiency.