Visible-infrared person re-identification (VI-ReID) is challenging due to
considerable cross-modality discrepancies. Existing works mainly focus on
learning modality-invariant features while suppressing modality-specific ones.
However, retrieving visible images only depends on infrared samples is an
extreme problem because of the absence of color information. To this end, we
present the Refer-VI-ReID settings, which aims to match target visible images
from both infrared images and coarse language descriptions (e.g., "a man with
red top and black pants") to complement the missing color information. To
address this task, we design a Y-Y-shape decomposition structure, dubbed YYDS,
to decompose and aggregate texture and color features of targets. Specifically,
the text-IoU regularization strategy is firstly presented to facilitate the
decomposition training, and a joint relation module is then proposed to infer
the aggregation. Furthermore, the cross-modal version of k-reciprocal
re-ranking algorithm is investigated, named CMKR, in which three neighbor
search strategies and one local query expansion method are explored to
alleviate the modality bias problem of the near neighbors. We conduct
experiments on SYSU-MM01, RegDB and LLCM datasets with our manually annotated
descriptions. Both YYDS and CMKR achieve remarkable improvements over SOTA
methods on all three datasets. Codes are available at
this https URL

通过引入 YYDS 结构来解决可见光 - 红外人员重新识别的问题，同时使用粗糙语言描述信息进行目标可见图像匹配，以补充缺失的颜色信息，并使用 CMKR 算法进行近邻模态偏差问题的改善。

YYDS：粗略描述的可见光红外人员再识别

YYDS: Visible-Infrared Person Re-Identification with Coarse Descriptions

Visible-infrared person re-identification (VI-ReID) is a challenging task due
to large cross-modality discrepancies and intra-class variations. Existing
methods mainly focus on learning modality-shared representations by embedding
different modalities into the same feature space. As a result, the learned
feature emphasizes the common patterns across modalities while suppressing
modality-specific and identity-aware information that is valuable for Re-ID. To
address these issues, we propose a novel Modality Unifying Network (MUN) to
explore a robust auxiliary modality for VI-ReID. First, the auxiliary modality
is generated by combining the proposed cross-modality learner and
intra-modality learner, which can dynamically model the modality-specific and
modality-shared representations to alleviate both cross-modality and
intra-modality variations. Second, by aligning identity centres across the
three modalities, an identity alignment loss function is proposed to discover
the discriminative feature representations. Third, a modality alignment loss is
introduced to consistently reduce the distribution distance of visible and
infrared images by modality prototype modeling. Extensive experiments on
multiple public datasets demonstrate that the proposed method surpasses the
current state-of-the-art methods by a significant margin.

通过引入一个新的模态统一网络（MUN），本研究针对可见光 - 红外人员重识别（VI-ReID）中的大量跨模态差异和类内变异问题，通过动态建模模态特定和模态共享表示来提取判别特征表达，从而使所提出的方法在多个公共数据集上明显超过当前最先进的方法。