Despite significant progress, image saliency detection still remains a challenging task in complex scenes and environments. Integrating multiple different but complementary cues, like RGB and Thermal infrared (RGBT), may be an effective way for boosting saliency detection performance. This work contributes a RGBT image dataset, which includes 821 spatially aligned RGBT image pairs and their ground truth annotations for saliency detection purpose. Moreover, 11 challenges are annotated on these image pairs for performing the challenge-sensitive analysis and 3 kinds of baseline methods are implemented to provide a comprehensive comparison platform. With this benchmark, we propose a novel approach based on a cooperative ranking algorithm for RGBT saliency detection. In particular, we introduce a weight for each modality to describe the reliability and a $\ell _{1}$ -based cross-modal consistency in a unified ranking model, and design an efficient solver to iteratively optimize several subproblems with closed-form solutions. Extensive experiments against baseline methods demonstrate the effectiveness of the proposed approach on both our introduced dataset and a public dataset.

中文翻译：

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RGBT 显着目标检测：基准和一种新的合作排序方法

尽管取得了重大进展，但图像显着性检测在复杂场景和环境中仍然是一项具有挑战性的任务。集成多个不同但互补的线索，如 RGB 和热红外 (RGBT)，可能是提高显着性检测性能的有效方法。这项工作贡献了一个 RGBT 图像数据集，其中包括 821 个空间对齐的 RGBT 图像对及其用于显着性检测目的的地面实况注释。此外，在这些图像对上注释了 11 个挑战，用于执行挑战敏感分析，并实施了 3 种基线方法，以提供一个全面的比较平台。有了这个基准，我们提出了一种基于合作排序算法的新方法，用于 RGBT 显着性检测。特别是，我们为每个模态引入了一个权重来描述统一排名模型中的可靠性和基于 $\ell_{1}$ 的跨模态一致性，并设计了一个高效的求解器来迭代优化具有封闭形式解决方案的几个子问题。针对基线方法的大量实验证明了所提出的方法在我们引入的数据集和公共数据集上的有效性。