Deep multimodal representation learning for generalizable person re-identification

摘要

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, we propose a Deep Multimodal Representation Learning network to elaborate rich semantic knowledge for assisting in representation learning during the pre-training. Importantly, a multimodal representation learning strategy is introduced to translate the features of different modalities into the common space, which can significantly