Multi-model Synergy Perception for Open-world Person Re-identification

Open-world person re-identification aims to train a model on source doamins and generalize well on unseen domains. Existing domain generalizable person re-identification methods primarily employ the equality training paradigm to train the model on multi-source domains. However, in open-world scenarios, domain imbalance often causes domain bias issue that leads to sub-optimal generalization ability, which is seriously overlooked. In this paper, we propose a Multi-model Synergy Perception (MSP) framework equipped with an Asynchronous Training Paradigm (ATP) on biased domains to maintain the domain balance for exploring the domain-invariant features. With the philosophy of divide and conquer, we divide the biased source domains into multiple debiased sub-source domains and employ a multi-network architecture to learn these sub-source domains in parallel. Additionally, to better generalize knowledge across these sub-source domains, we propose a Structure Synergy Perception (SSP) module that constructs the feature relationship distribution for each sub-domain and aligns them to map the unique knowledge to each other. Furthermore, considering the consistency of sub-source domains, we further propose a Synergy Distillation Perception (SDP) to improve the model both semantic and domain generalization ability. The main idea of SDP is to use the center guided soft label and the part based triplet graph to distill each submodel, which can facilitate the network to explore domain-invariant representations of images. Extensive experiments demonstrate that our method outperforms state-of-the-arts for open-domain person ReID.