  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
https://github.com/dyhBUPT/YYDS.
