论文标题
RGB - 信号跨模式人员重新识别的相似性推理指标
A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification
论文作者
论文摘要
旨在在RGB画廊中搜索IR图像的RGB-Infrared（IR）跨模式重新识别（RE-ID），反之亦然，由于IR和RGB模式之间的差异很大，这是一项艰巨的任务。现有方法通常通过将特征分布或图像样式对准跨模态来应对这一挑战，而在同一模态的画廊样本（即模式内样本相似性）中，非常有用的相似性在很大程度上被忽略了。本文提出了一种新颖的相似性推理指标（SIM），该指标（SIM）利用了模式内样品相似性，以规避跨模式差异，以靶向最佳的跨模式图像匹配。 SIM通过连续的相似性图形推理和相互的最近邻里推理，通过从两个不同的角度利用模式内样本相似性来实现跨模式相似性。在两个跨模式重新ID数据集（SYSU-MM01和REGDB）上进行的广泛实验表明，SIM可实现明显的准确性提高，但与最先进的ART相比，几乎没有额外的培训。
RGB-Infrared (IR) cross-modality person re-identification (re-ID), which aims to search an IR image in RGB gallery or vice versa, is a challenging task due to the large discrepancy between IR and RGB modalities. Existing methods address this challenge typically by aligning feature distributions or image styles across modalities, whereas the very useful similarities among gallery samples of the same modality (i.e. intra-modality sample similarities) is largely neglected. This paper presents a novel similarity inference metric (SIM) that exploits the intra-modality sample similarities to circumvent the cross-modality discrepancy targeting optimal cross-modality image matching. SIM works by successive similarity graph reasoning and mutual nearest-neighbor reasoning that mine cross-modality sample similarities by leveraging intra-modality sample similarities from two different perspectives. Extensive experiments over two cross-modality re-ID datasets (SYSU-MM01 and RegDB) show that SIM achieves significant accuracy improvement but with little extra training as compared with the state-of-the-art.
