Drone-based vehicle detection aims at finding the vehicle locations and
categories in an aerial image. It empowers smart city traffic management and
disaster rescue. Researchers have made mount of efforts in this area and
achieved considerable progress. Nevertheless, it is still a challenge when the
objects are hard to distinguish, especially in low light conditions. To tackle
this problem, we construct a large-scale drone-based RGB-Infrared vehicle
detection dataset, termed DroneVehicle. Our DroneVehicle collects 28, 439
RGB-Infrared image pairs, covering urban roads, residential areas, parking
lots, and other scenarios from day to night. Due to the great gap between RGB
and infrared images, cross-modal images provide both effective information and
redundant information. To address this dilemma, we further propose an
uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to
extract complementary information from cross-modal images, which can
significantly improve the detection performance in low light conditions. An
uncertainty-aware module (UAM) is designed to quantify the uncertainty weights
of each modality, which is calculated by the cross-modal Intersection over
Union (IoU) and the RGB illumination value. Furthermore, we design an
illumination-aware cross-modal non-maximum suppression algorithm to better
integrate the modal-specific information in the inference phase. Extensive
experiments on the DroneVehicle dataset demonstrate the flexibility and
effectiveness of the proposed method for crossmodality vehicle detection. The
dataset can be download from this https URL

该研究构建了一个大规模的无人机 RGB - 红外车辆检测数据集，在低光条件下，使用不确定性感知的跨模态车辆检测框架提取交叉模态图像的补充信息，以大幅提高检测性能。

基于无人机的 RGB - 红外异模态车辆检测：基于不确定性感知的学习

Drone-based RGB-Infrared Cross-Modality Vehicle Detection via  Uncertainty-Aware Learning

RGB-Infrared (IR) person re-identification is very challenging due to the
large cross-modality variations between RGB and IR images. The key solution is
to learn aligned features to the bridge RGB and IR modalities. However, due to
the lack of correspondence labels between every pair of RGB and IR images, most
methods try to alleviate the variations with set-level alignment by reducing
the distance between the entire RGB and IR sets. However, this set-level
alignment may lead to misalignment of some instances, which limits the
performance for RGB-IR Re-ID. Different from existing methods, in this paper,
we propose to generate cross-modality paired-images and perform both global
set-level and fine-grained instance-level alignments. Our proposed method
enjoys several merits. First, our method can perform set-level alignment by
disentangling modality-specific and modality-invariant features. Compared with
conventional methods, ours can explicitly remove the modality-specific features
and the modality variation can be better reduced. Second, given cross-modality
unpaired-images of a person, our method can generate cross-modality paired
images from exchanged images. With them, we can directly perform instance-level
alignment by minimizing distances of every pair of images. Extensive
experimental results on two standard benchmarks demonstrate that the proposed
model favourably against state-of-the-art methods. Especially, on SYSU-MM01
dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and
mAP. Code is available at this https URL

本研究提出了一种新方法，通过生成跨模态的配对图像并进行全局和细粒度实例级别的对齐，使 RGB-IR Re-ID 的性能得到改善，实验证明该方法在两个标准基准测试中表现优于现有的最新技术。

RGB - 红外人员再识别中的跨模态配对图像生成

Cross-Modality Paired-Images Generation for RGB-Infrared Person  Re-Identification

The problem of cross-modality person re-identification has been receiving
increasing attention recently, due to its practical significance. Motivated by
the fact that human usually attend to the difference when they compare two
similar objects, we propose a dual-path cross-modality feature learning
framework which preserves intrinsic spatial strictures and attends to the
difference of input cross-modality image pairs. Our framework is composed by
two main components: a Dual-path Spatial-structure-preserving Common Space
Network (DSCSN) and a Contrastive Correlation Network (CCN). The former embeds
cross-modality images into a common 3D tensor space without losing spatial
structures, while the latter extracts contrastive features by dynamically
comparing input image pairs. Note that the representations generated for the
input RGB and Infrared images are mutually dependant to each other. We conduct
extensive experiments on two public available RGB-IR ReID datasets, SYSU-MM01
and RegDB, and our proposed method outperforms state-of-the-art algorithms by a
large margin with both full and simplified evaluation modes.

该研究提出了一种双通道跨模式特征学习框架，旨在保留内在空间结构并关注输入交叉模态图像对的差异，通过在一个共同的 3D 张量空间中嵌入交叉模式图像而不丢失空间结构以及通过动态比较输入图像对来提取对比特征等方式，在两个公共可用的 RGB-IR ReID 数据集上进行了广泛实验，并在全面和简化的评估模式下均优于现有的算法。