Visible-Infrared Person Re-identification (VI-ReID) is a challenging
cross-modal pedestrian retrieval task, due to significant intra-class
variations and cross-modal discrepancies among different cameras. Existing
works mainly focus on embedding images of different modalities into a unified
space to mine modality-shared features. They only seek distinctive information
within these shared features, while ignoring the identity-aware useful
information that is implicit in the modality-specific features. To address this
issue, we propose a novel Implicit Discriminative Knowledge Learning (IDKL)
network to uncover and leverage the implicit discriminative information
contained within the modality-specific. First, we extract modality-specific and
modality-shared features using a novel dual-stream network. Then, the
modality-specific features undergo purification to reduce their modality style
discrepancies while preserving identity-aware discriminative knowledge.
Subsequently, this kind of implicit knowledge is distilled into the
modality-shared feature to enhance its distinctiveness. Finally, an alignment
loss is proposed to minimize modality discrepancy on enhanced modality-shared
features. Extensive experiments on multiple public datasets demonstrate the
superiority of IDKL network over the state-of-the-art methods. Code is
available at this https URL

可见光红外人物再识别是一项具有挑战性的跨模态行人检索任务，该研究提出了一种新颖的隐式判别知识学习网络 (IDKL)，通过提取模态特定和模态共享特征，减少模态风格差异并提升识别知识，实现了对隐含判别信息的利用，进一步强化模态共享特征的独特性，以最小化模态差异。与现有方法相比，实验结果表明 IDKL 网络具有优越性能。

可见 - 红外人物再识别的隐性判别知识学习

Implicit Discriminative Knowledge Learning for Visible-Infrared Person  Re-Identification

RGB-Thermal (RGB-T) semantic segmentation has shown great potential in
handling low-light conditions where RGB-based segmentation is hindered by poor
RGB imaging quality. The key to RGB-T semantic segmentation is to effectively
leverage the complementarity nature of RGB and thermal images. Most existing
algorithms fuse RGB and thermal information in feature space via concatenation,
element-wise summation, or attention operations in either unidirectional
enhancement or bidirectional aggregation manners. However, they usually
overlook the modality gap between RGB and thermal images during feature fusion,
resulting in modality-specific information from one modality contaminating the
other. In this paper, we propose a Channel and Spatial Relation-Propagation
Network (CSRPNet) for RGB-T semantic segmentation, which propagates only
modality-shared information across different modalities and alleviates the
modality-specific information contamination issue. Our CSRPNet first performs
relation-propagation in channel and spatial dimensions to capture the
modality-shared features from the RGB and thermal features. CSRPNet then
aggregates the modality-shared features captured from one modality with the
input feature from the other modality to enhance the input feature without the
contamination issue. While being fused together, the enhanced RGB and thermal
features will be also fed into the subsequent RGB or thermal feature extraction
layers for interactive feature fusion, respectively. We also introduce a
dual-path cascaded feature refinement module that aggregates multi-layer
features to produce two refined features for semantic and boundary prediction.
Extensive experimental results demonstrate that CSRPNet performs favorably
against state-of-the-art algorithms.

RGB-Thermal 语义分割中存在模态间信息交叉污染问题，本文提出了一种基于信道和空间关系传播网络（CSRPNet）的解决方案，通过在信道和空间维度进行关系传播，捕捉 RGB 和热成像图像的模态共享特征，并通过增强输入特征的方式解决了信息污染问题。同时，引入了双路径级联特征细化模块来进行多层特征聚合，产生用于语义和边界预测的两个细化特征。大量实验证明，CSRPNet 在各方面优于现有的算法。

基于通道和空间关系传播网络的 RGB - 热红外语义分割

Channel and Spatial Relation-Propagation Network for RGB-Thermal  Semantic Segmentation

Visible-Infrared Person Re-Identification (VI-ReID) is a challenging
retrieval task under complex modality changes. Existing methods usually focus
on extracting discriminative visual features while ignoring the reliability and
commonality of visual features between different modalities. In this paper, we
propose a novel deep learning framework named Progressive Modality-shared
Transformer (PMT) for effective VI-ReID. To reduce the negative effect of
modality gaps, we first take the gray-scale images as an auxiliary modality and
propose a progressive learning strategy. Then, we propose a Modality-Shared
Enhancement Loss (MSEL) to guide the model to explore more reliable identity
information from modality-shared features. Finally, to cope with the problem of
large intra-class differences and small inter-class differences, we propose a
Discriminative Center Loss (DCL) combined with the MSEL to further improve the
discrimination of reliable features. Extensive experiments on SYSU-MM01 and
RegDB datasets show that our proposed framework performs better than most
state-of-the-art methods. For model reproduction, we release the source code at
this https URL

本文提出了一种名为 PMT 的深度学习框架，它采用渐进式共享模态转换器来解决 VI-ReID 任务中复杂的模态变化问题，并对模态共享特征进行可靠度和共性分析；同时提出了一种 Discriminative Center Loss (DCL)，结合 Modality-Shared Enhancement Loss (MSEL) 来缓解大内部差异和小类间差异问题，从而更好地区分共享特征，该框架在 SYSU-MM01 和 RegDB 数据集上的表现优于现有的大多数最新状态 - of-the-art 方法。