The RGB-infrared cross-modality person re-identification (ReID) task aims to
recognize the images of the same identity between the visible modality and the
infrared modality. Existing methods mainly use a two-stream architecture to
eliminate the discrepancy between the two modalities in the final common
feature space, which ignore the single space of each modality in the shallow
layers. To solve it, in this paper, we present a novel multi-feature space
joint optimization (MSO) network, which can learn modality-sharable features in
both the single-modality space and the common space. Firstly, based on the
observation that edge information is modality-invariant, we propose an edge
features enhancement module to enhance the modality-sharable features in each
single-modality space. Specifically, we design a perceptual edge features (PEF)
loss after the edge fusion strategy analysis. According to our knowledge, this
is the first work that proposes explicit optimization in the single-modality
feature space on cross-modality ReID task. Moreover, to increase the difference
between cross-modality distance and class distance, we introduce a novel
cross-modality contrastive-center (CMCC) loss into the modality-joint
constraints in the common feature space. The PEF loss and CMCC loss jointly
optimize the model in an end-to-end manner, which markedly improves the
network's performance. Extensive experiments demonstrate that the proposed
model significantly outperforms state-of-the-art methods on both the SYSU-MM01
and RegDB datasets.

本文提出了一种新颖的多特征空间联合优化（MSO）网络，在单模态空间和公共空间中学习可共享特征，通过感知边缘特征（PEF）损失和交叉模态对比中心（CMCC）损失共同优化模型，显着提高了网络性能，在 cross-modality person re-identification 任务上优于现有的最先进方法。

RGB - 红外人员再识别的多特征空间联合优化网络 MSO

MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared  Person Re-Identification

Cross-modality person re-identification (cm-ReID) is a challenging but key
technology for intelligent video analysis. Existing works mainly focus on
learning common representation by embedding different modalities into a same
feature space. However, only learning the common characteristics means great
information loss, lowering the upper bound of feature distinctiveness. In this
paper, we tackle the above limitation by proposing a novel cross-modality
shared-specific feature transfer algorithm (termed cm-SSFT) to explore the
potential of both the modality-shared information and the modality-specific
characteristics to boost the re-identification performance. We model the
affinities of different modality samples according to the shared features and
then transfer both shared and specific features among and across modalities. We
also propose a complementary feature learning strategy including modality
adaption, project adversarial learning and reconstruction enhancement to learn
discriminative and complementary shared and specific features of each modality,
respectively. The entire cm-SSFT algorithm can be trained in an end-to-end
manner. We conducted comprehensive experiments to validate the superiority of
the overall algorithm and the effectiveness of each component. The proposed
algorithm significantly outperforms state-of-the-arts by 22.5% and 19.3% mAP on
the two mainstream benchmark datasets SYSU-MM01 and RegDB, respectively.

本文提出了一种新的跨模态共享专用特征传输算法（cm-SSFT），通过模型共享特征和模型特定特征的转移，包括模态自适应、对抗学习和重构增强，学习每个模态的区分性和互补共享特征。综合实验结果表明，该算法显著优于现有算法， 在 SYSU-MM01 和 RegDB 数据集上分别提高了 22.5% 和 19.3% 的平均精度。

共享特定特征传递的跨模态人员重新识别

Cross-modality Person re-identification with Shared-Specific Feature  Transfer

Cross-modality person re-identification is a challenging problem which
retrieves a given pedestrian image in RGB modality among all the gallery images
in infrared modality. The task can address the limitation of RGB-based person
Re-ID in dark environments. Existing researches mainly focus on enlarging
inter-class differences of feature to solve the problem. However, few studies
investigate improving intra-class cross-modality similarity, which is important
for this issue. In this paper, we propose a novel loss function, called
Hetero-Center loss (HC loss) to reduce the intra-class cross-modality
variations. Specifically, HC loss can supervise the network learning the
cross-modality invariant information by constraining the intra-class center
distance between two heterogenous modalities. With the joint supervision of
Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two
vital objectives, inter-class discrepancy and intra-class cross-modality
similarity as much as possible. Besides, we propose a simple and
high-performance network architecture to learn local feature representations
for cross-modality person re-identification, which can be a baseline for future
research. Extensive experiments indicate the effectiveness of the proposed
methods, which outperform state-of-the-art methods by a wide margin.

本文提出了一种新的损失函数，叫做 Hetero-Center loss（HC loss），用于减少跨模态内类别的差异，并提出了一种简单高效的网络架构，用于学习跨模态人员重新识别的本地特征表示，得到了比现有方法更好的结果。