  RGB-Infrared person re-identification (RGB-IR Re-ID) aims to match persons
from heterogeneous images captured by visible and thermal cameras, which is of
great significance in the surveillance system under poor light conditions.
Facing great challenges in complex variances including conventional
single-modality and additional inter-modality discrepancies, most of the
existing RGB-IR Re-ID methods propose to impose constraints in image level,
feature level or a hybrid of both. Despite the better performance of hybrid
constraints, they are usually implemented with heavy network architecture. As a
matter of fact, previous efforts contribute more as pioneering works in new
cross-modal Re-ID area while leaving large space for improvement. This can be
mainly attributed to: (1) lack of abundant person image pairs from different
modalities for training, and (2) scarcity of salient modality-invariant
features especially on coarse representations for effective matching. To
address these issues, a novel Multi-Scale Part-Aware Cascading framework
(MSPAC) is formulated by aggregating multi-scale fine-grained features from
part to global in a cascading manner, which results in a unified representation
containing rich and enhanced semantic features. Furthermore, a marginal
exponential centre (MeCen) loss is introduced to jointly eliminate mixed
variances from intra- and inter-modal examples. Cross-modality correlations can
thus be efficiently explored on salient features for distinctive
modality-invariant feature learning. Extensive experiments are conducted to
demonstrate that the proposed method outperforms all the state-of-the-art by a
large margin.
