论文标题
强大但简单的基线，可见的热人重新识别双重粒度三重损失
Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification
论文作者
论文摘要
在这封信中，我们提出了一个概念上简单有效的双重粒度三胞胎损失（可见的热人重新识别（VT-REID））。通常，REID模型始终受到基于样本的三重态损失和识别粒子水平的识别损失的训练。当引入基于中心的损失以鼓励与粗粒度水平的阶层内歧视和阶层歧视时，这是可能的。我们提出的双重粒度三重损失很好地组织了基于样本的三重态损失和中心三重态损失，并以层次的罚款至粗粒度方式，仅具有一些简单的典型操作配置，例如合并和批处理归一化。 REGDB和SYSU-MM01数据集的实验表明，只有全球功能，我们的双粒度三重态损耗才能提高VT-REID性能的显着余量。这可能是强大的VT固定基线，可以提高未来的研究。
In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID). In general, ReID models are always trained with the sample-based triplet loss and identification loss from the fine granularity level. It is possible when a center-based loss is introduced to encourage the intra-class compactness and inter-class discrimination from the coarse granularity level. Our proposed dual-granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to coarse granularity manner, just with some simple configurations of typical operations, such as pooling and batch normalization. Experiments on RegDB and SYSU-MM01 datasets show that with only the global features our dual-granularity triplet loss can improve the VT-ReID performance by a significant margin. It can be a strong VT-ReID baseline to boost future research with high quality.
