Visible-infrared person re-identification (VI-ReID) aims to match people with
the same identity between visible and infrared modalities. VI-ReID is a
challenging task due to the large differences in individual appearance under
different modalities. Existing methods generally try to bridge the cross-modal
differences at image or feature level, which lacks exploring the discriminative
embeddings. Effectively minimizing these cross-modal discrepancies relies on
obtaining representations that are guided by identity and consistent across
modalities, while also filtering out representations that are irrelevant to
identity. To address these challenges, we introduce a dynamic identity-guided
attention network (DIAN) to mine identity-guided and modality-consistent
embeddings, facilitating effective bridging the gap between different
modalities. Specifically, in DIAN, to pursue a semantically richer
representation, we first use orthogonal projection to fuse the features from
two connected coarse and fine layers. Furthermore, we first use dynamic
convolution kernels to mine identity-guided and modality-consistent
representations. More notably, a cross embedding balancing loss is introduced
to effectively bridge cross-modal discrepancies by above embeddings.
Experimental results on SYSU-MM01 and RegDB datasets show that DIAN achieves
state-of-the-art performance. Specifically, for indoor search on SYSU-MM01, our
method achieves 86.28% rank-1 accuracy and 87.41% mAP, respectively. Our code
will be available soon.

多模态人物识别中的动态身份引导注意力网络（DIAN）旨在通过挖掘身份引导和模态一致的嵌入来解决交叉模态差异的问题。实验结果表明，DIAN 在 SYSU-MM01 和 RegDB 数据集上取得了最先进的性能。

动态身份引导注意力网络用于可见 - 红外人物再识别

Dynamic Identity-Guided Attention Network for Visible-Infrared Person  Re-identification

We address the problem of visible-infrared person re-identification
(VI-reID), that is, retrieving a set of person images, captured by visible or
infrared cameras, in a cross-modal setting. Two main challenges in VI-reID are
intra-class variations across person images, and cross-modal discrepancies
between visible and infrared images. Assuming that the person images are
roughly aligned, previous approaches attempt to learn coarse image- or rigid
part-level person representations that are discriminative and generalizable
across different modalities. However, the person images, typically cropped by
off-the-shelf object detectors, are not necessarily well-aligned, which
distract discriminative person representation learning. In this paper, we
introduce a novel feature learning framework that addresses these problems in a
unified way. To this end, we propose to exploit dense correspondences between
cross-modal person images. This allows to address the cross-modal discrepancies
in a pixel-level, suppressing modality-related features from person
representations more effectively. This also encourages pixel-wise associations
between cross-modal local features, further facilitating discriminative feature
learning for VI-reID. Extensive experiments and analyses on standard VI-reID
benchmarks demonstrate the effectiveness of our approach, which significantly
outperforms the state of the art.

本研究提出了一种新颖的特征学习框架，通过利用密集对应的交叉模态人物图像，像素级地抑制与模态相关的特征，从而更有效地促进交叉模态本地特征的判别式特征学习，从而解决了可见 - 红外人物重识别的问题。