  Visible-infrared person re-identification (VI-ReID) is a task of matching the
same individuals across the visible and infrared modalities. Its main challenge
lies in the modality gap caused by cameras operating on different spectra.
Existing VI-ReID methods mainly focus on learning general features across
modalities, often at the expense of feature discriminability. To address this
issue, we present a novel cycle-construction-based network for neutral yet
discriminative feature learning, termed CycleTrans. Specifically, CycleTrans
uses a lightweight Knowledge Capturing Module (KCM) to capture rich semantics
from the modality-relevant feature maps according to pseudo queries.
Afterwards, a Discrepancy Modeling Module (DMM) is deployed to transform these
features into neutral ones according to the modality-irrelevant prototypes. To
ensure feature discriminability, another two KCMs are further deployed for
feature cycle constructions. With cycle construction, our method can learn
effective neutral features for visible and infrared images while preserving
their salient semantics. Extensive experiments on SYSU-MM01 and RegDB datasets
validate the merits of CycleTrans against a flurry of state-of-the-art methods,
+4.57% on rank-1 in SYSU-MM01 and +2.2% on rank-1 in RegDB.
