论文标题
RGB - 融资人员重新识别的双向指数角损失
Bi-directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification
论文作者
论文摘要
RGB-Infrared人员重新识别（RGB-IR重新识别）是一个跨模式匹配的问题，在这种问题中，模式差异是一个巨大的挑战。大多数现有的作品都使用基于欧几里得的限制来解决来自不同模式的图像特征之间的差异。但是，这些方法无法学习角度歧视特征嵌入，因为欧几里得距离无法有效地测量嵌入载体之间的随附角度。作为一个有角度歧视的特征空间对于基于人类嵌入向量分类的人类图像很重要，在本文中，我们提出了一种新颖的排名损失函数，称为双向指数的角度角损失，以帮助学习一个可分离的共同特征空间，通过在嵌入量之间的嵌入角度显式地约束包含的角度。此外，为了帮助稳定和学习嵌入向量的幅度，我们采用了一个公共空间批发层。 SYSU-MM01和REGDB数据集的定量和定性实验支持我们的分析。在SYSU-MM01数据集上，与基线相比，Rank-1精度 / MAP的性能从7.40％ / 11.46％提高到38.57％ / 38.61％。提出的方法可以推广到单模性重新ID的任务，并将Cark-1准确性 /地图从市场1501数据集的92.0％ / 81.7％提高到94.7％ / 86.6％，从dukemtmc-redaset的Dukemtmc-redaset上的82.6％ / 70.6％到87.6％ / 77.1％。代码：https：//github.com/prismformore/expat
RGB-Infrared person re-identification (RGB-IR Re- ID) is a cross-modality matching problem, where the modality discrepancy is a big challenge. Most existing works use Euclidean metric based constraints to resolve the discrepancy between features of images from different modalities. However, these methods are incapable of learning angularly discriminative feature embedding because Euclidean distance cannot measure the included angle between embedding vectors effectively. As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors. Moreover, to help stabilize and learn the magnitudes of embedding vectors, we adopt a common space batch normalization layer. The quantitative and qualitative experiments on the SYSU-MM01 and RegDB dataset support our analysis. On SYSU-MM01 dataset, the performance is improved from 7.40% / 11.46% to 38.57% / 38.61% for rank-1 accuracy / mAP compared with the baseline. The proposed method can be generalized to the task of single-modality Re-ID and improves the rank-1 accuracy / mAP from 92.0% / 81.7% to 94.7% / 86.6% on the Market-1501 dataset, from 82.6% / 70.6% to 87.6% / 77.1% on the DukeMTMC-reID dataset. Code: https://github.com/prismformore/expAT
