先解耦再解析：基于光照解耦的夜间语义分割
Disentangle then Parse:Night-time Semantic Segmentation with Illumination Disentanglement
AI总结：
针对现有语义分割方法夜间场景表现不佳的问题，提出“先解耦再解析”范式，通过语义导向解耦提取光照不变反射分量，结合光照感知解析器聚合光照特征，显著提升夜间分割精度且参量开销极低。
摘要：
大多数现有语义分割方法都是针对白天场景开发的，由于光照条件不足且复杂，这类方法在夜间场景中通常表现不佳。在本研究中，我们提出了一种全新的夜间语义分割范式，即先解耦再解析（disentangle then parse, DTP），以应对这一挑战。DTP 显式地将夜间图像解耦为光照不变的反射分量和光照特定的光照分量，随后基于二者的自适应融合进行语义识别。具体而言，所提出的 DTP 包含两个核心组件：1）不同于现有方法处理光照耦合特征的方式，我们的语义导向解耦（Semantic-Oriented Disentanglement, SOD）框架能够提取不受光照干扰的反射分量，使网络在多变复杂的光照条件下也能稳定识别语义。2）基于光照分量可作为部分语义混淆区域的识别线索这一观察，我们进一步引入光照感知解析器（Illumination-Aware Parser, IAParser），显式学习语义与光照之间的关联，并聚合光照特征以生成更精准的预测结果。在多种设置下开展的夜间分割任务大量实验表明，DTP 的性能显著优于当前最优方法。此外，DTP 仅增加可忽略不计的参数量，即可直接用于提升现有白天方法的夜间分割效果。
英文摘要：
Most prior semantic segmentation methods have been developed for day-time scenes, while typically underperforming in night-time scenes due to insufficient and complicated lighting conditions. In this work, we tackle this challenge by proposing a novel night-time semantic segmentation paradigm, i.e., disentangle then parse (DTP). DTP explicitly disentangles night-time images into light-invariant reflectance and light-specific illumination components and then recognizes semantics based on their adaptive fusion. Concretely, the proposed DTP comprises two key components: 1) Instead of processing lighting-entangled features as in prior works, our Semantic-Oriented Disentanglement (SOD) framework enables the extraction of reflectance component without being impeded by lighting, allowing the network to consistently recognize the semantics under cover of varying and complicated lighting conditions. 2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions. Extensive experiments on the night-time segmentation task with various settings demonstrate that DTP significantly outperforms state-of-the-art methods. Furthermore, with negligible additional parameters, DTP can be directly used to benefit existing day-time methods for night-time segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
