Visible-infrared person re-identification (VI-ReID) aims to search the same
pedestrian of interest across visible and infrared modalities. Existing models
mainly focus on compensating for modality-specific information to reduce
modality variation. However, these methods often lead to a higher computational
overhead and may introduce interfering information when generating the
corresponding images or features. To address this issue, it is critical to
leverage pedestrian-attentive features and learn modality-complete and
-consistent representation. In this paper, a novel Transferring Modality-Aware
Pedestrian Attentive Learning (TMPA) model is proposed, focusing on the
pedestrian regions to efficiently compensate for missing modality-specific
features. Specifically, we propose a region-based data augmentation module
PedMix to enhance pedestrian region coherence by mixing the corresponding
regions from different modalities. A lightweight hybrid compensation module,
i.e., the Modality Feature Transfer (MFT), is devised to integrate cross
attention and convolution networks to fully explore the discriminative
modality-complete features with minimal computational overhead. Extensive
experiments conducted on the benchmark SYSU-MM01 and RegDB datasets
demonstrated the effectiveness of our proposed TMPA model.

通过引入区域数据增强和轻量级混合补偿模块的转移感知行人关注学习 (TMPA) 模型，以有效补偿缺失的特定模态特征，并完全探索具有最小计算开销的判别模块完整特征，我们提出了一种新颖的可见 - 红外行人再识别方法。在 SYSU-MM01 和 RegDB 数据集上进行的广泛实验证明了我们提出的 TMPA 模型的有效性。

跨模态感知行人注视学习的可见光 - 红外人物重新识别

Transferring Modality-Aware Pedestrian Attentive Learning  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

The task of RGBT tracking aims to take the complementary advantages from
visible spectrum and thermal infrared data to achieve robust visual tracking,
and receives more and more attention in recent years. Existing works focus on
modality-specific information integration by introducing modality weights to
achieve adaptive fusion or learning robust feature representations of different
modalities. Although these methods could effectively deploy the
modality-specific properties, they ignore the potential values of
modality-shared cues as well as instance-aware information, which are crucial
for effective fusion of different modalities in RGBT tracking. In this paper,
we propose a novel Multi-Adapter convolutional Network (MANet) to jointly
perform modality-shared, modality-specific and instance-aware feature learning
in an end-to-end trained deep framework for RGBT tracking. We design three
kinds of adapters within our network. In a specific, the generality adapter is
to extract shared object representations, the modality adapter aims at encoding
modality-specific information to deploy their complementary advantages, and the
instance adapter is to model the appearance properties and temporal variations
of a certain object. Moreover, to reduce computational complexity for real-time
demand of visual tracking, we design a parallel structure of generic adapter
and modality adapter. Extensive experiments on two RGBT tracking benchmark
datasets demonstrate the outstanding performance of the proposed tracker
against other state-of-the-art RGB and RGBT tracking algorithms.

该论文提出一种多适配器卷积网络（MANet），用于 RGBT 跟踪中的模态共享、模态特定和实例感知特征学习，以及采用并行结构的适配器以减少计算复杂度，与其他 RGB 和 RGBT 跟踪算法相比表现出杰出的性能。