Foundation models like the Segment Anything Model (SAM) demonstrate impressive zero-shot generalization but frequently degrade under diverse real-world illumination, particularly for instance segmentation. In this work, we address this limitation by developing \textit{Lighting Convolutional-Attention (\lca{})}, an adapter module that enhances segmentation robustness without fine-tuning the heavy backbone. \lca{} employs a dual-branch architecture to process RGB features alongside contrast maps, enabling physically motivated sensitivity to structural changes rather than illumination artifacts. We optimize \lca{} through a pairwise training strategy, introducing a targeted loss term that explicitly penalizes discrepancies between clean images and their corresponding illumination variants. To evaluate and support this architecture, we conduct a comprehensive empirical study across multiple existing benchmarks and present a novel Unity-based synthetic dataset specifically designed to accurately replicate complex real-world lighting conditions. Extensive experimental results demonstrate that our approach successfully bridges the domain gap, delivering superior lighting-robust segmentation.

翻译：基础模型（如Segment Anything Model, SAM）在零样本泛化中展现出卓越性能，但在复杂现实光照条件下（尤其是实例分割任务中）性能显著下降。针对这一局限，本文提出**光照卷积注意力模块（LCA）**——一种无需微调重型骨干网络即可增强分割鲁棒性的适配模块。LCA采用双分支架构同步处理RGB特征与对比度图谱，能基于物理机制感知结构变化而非光照伪影。我们通过成对训练策略优化LCA，引入针对性损失项显式惩罚干净图像与其光照变体间的差异。为验证并支撑该架构，我们在多个现有基准上开展系统性实证研究，并构建基于Unity的新型合成数据集，该数据集精确复现复杂现实光照条件。大量实验结果表明，本方法成功弥合领域差距，实现了鲁棒的光照适应分割性能。