针对复杂天气下的显著性检测，提出噪声指示融合模块提升精度。
Salient Object Detection in Complex Weather Conditions via Noise Indicators
- 用噪声指示向量融合天气类型信息，动态调节特征提取。
- 在不同训练数据比例下，显著提升复杂天气场景的分割准确率。
- 适配主流解码器，可直接用于真实天气下的视觉任务部署。
显著性物体检测（SOD）作为计算机视觉基础任务，已从单模态发展到多模态以增强泛化能力。然而，现有方法大多假设低噪声视觉条件，忽视了真实场景中天气引发的噪声对分割精度的下降影响。本文提出一种专用于多样天气条件的SOD框架，包含特定编码器和可替换解码器。为处理不同天气噪声，引入一热向量作为噪声指示符，并设计噪声指示融合模块（NIFM）。该模块将语义特征与噪声指示符作为双输入，在编码器连续阶段间嵌入天气感知先验，通过自适应特征调制实现。关键的是，所提特定编码器保持与主流SOD解码器的兼容性。在WXSOD数据集上进行大量实验，涵盖不同训练数据规模（100%、50%、30%全训练集）、三种编码器和七种解码器配置。结果表明，该框架（尤其是集成NIFM的特定编码器）在复杂天气条件下相比基线编码器显著提升了分割精度。
原文摘要 · Abstract (English)
Salient object detection (SOD), a foundational task in computer vision, has advanced from single-modal to multi-modal paradigms to enhance generalization. However, most existing SOD methods assume low-noise visual conditions, overlooking the degradation of segmentation accuracy caused by weather-induced noise in real-world scenarios. In this paper, we propose a SOD framework tailored for diverse weather conditions, encompassing a specific encoder and a replaceable decoder. To enable handling of varying weather noises, we introduce a one-hot vector as a noise indicator to represent different weather types and design a Noise Indicator Fusion Module (NIFM). The NIFM takes both semantic features and the noise indicator as dual inputs and is inserted between consecutive stages of the encoder to embed weather-aware priors via adaptive feature modulation. Critically, the proposed specific encoder retains compatibility with mainstream SOD decoders. Extensive experiments are conducted on the WXSOD dataset under varying training data scales (100%, 50%, 30% of the full training set), three encoder and seven decoder configurations. Results show that the proposed SOD framework (particularly the NIFM-enhanced specific encoder) improves segmentation accuracy under complex weather conditions compared to a vanilla encoder.
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