Many existing multi-modality studies are based on the assumption of modality
integrity. However, the problem of missing arbitrary modalities is very common
in real life, and this problem is less studied, but actually important in the
task of multi-modality person re-identification (Re-ID). To this end, we design
a novel dynamic enhancement network (DENet), which allows missing arbitrary
modalities while maintaining the representation ability of multiple modalities,
for partial multi-modality person Re-ID. To be specific, the multi-modal
representation of the RGB, near-infrared (NIR) and thermal-infrared (TIR)
images is learned by three branches, in which the information of missing
modalities is recovered by the feature transformation module. Since the missing
state might be changeable, we design a dynamic enhancement module, which
dynamically enhances modality features according to the missing state in an
adaptive manner, to improve the multi-modality representation. Extensive
experiments on multi-modality person Re-ID dataset RGBNT201 and vehicle Re-ID
dataset RGBNT100 comparing to the state-of-the-art methods verify the
effectiveness of our method in complex and changeable environments.

通过设计动态增强网络（DENet），可以在缺失任意模态的情况下，仍然保持多种方式的表示能力，用于部分多模态人员重新识别，并在 RGBNT201 数据集上进行广泛实验。