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تسجيل مستخدم جديدGeneralizable Person Re-identification with Relevance-aware Mixture of Experts
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Domain generalizable (DG) person re-identification (ReID) is a challenging problem because we cannot access any unseen target domain data during training. Almost all the existing DG ReID methods follow the same pipeline where they use a hybrid dataset from multiple source domains for training, and then directly apply the trained model to the unseen target domains for testing. These methods often neglect individual source domains discriminative characteristics and their relevances w.r.t. the unseen target domains, though both of which can be leveraged to help the models generalization. To handle the above two issues, we propose a novel method called the relevance-aware mixture of experts (RaMoE), using an effective voting-based mixture mechanism to dynamically leverage source domains diverse characteristics to improve the models generalization. Specifically, we propose a decorrelation loss to make the source domain networks (experts) keep the diversity and discriminability of individual domains characteristics. Besides, we design a voting network to adaptively integrate all the experts features into the more generalizable aggregated features with domain relevance. Considering the target domains invisibility during training, we propose a novel learning-to-learn algorithm combined with our relation alignment loss to update the voting network. Extensive experiments demonstrate that our proposed RaMoE outperforms the state-of-the-art methods.
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Existing person re-identification (re-id) methods are stuck when deployed to a new unseen scenario despite the success in cross-camera person matching. Recent efforts have been substantially devoted to domain adaptive person re-id where extensive unl
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ess this, this paper presents a solution, called DomainMix, which can learn a person re-identification model from both synthetic and real-world data, for the first time, completely without human annotations. This way, the proposed method enjoys the cheap availability of large-scale training data, and benefiting from its scalability and diversity, the learned model is able to generalize well on unseen domains. Specifically, inspired from a recent work generating large-scale synthetic data for effective person re-identification training, in each epoch, the proposed method firstly clusters the unlabeled real-world images and select the reliable clusters according to three criteria, i.e. independence, compactness, and quantity. Then, the classification layer is initialized adaptively using the generated features of real-world images. When training, to address the large domain gap between two domains, a domain-invariant feature learning method is proposed, which designs an adversarial learning between domain-invariant feature learning and domain discrimination, and meanwhile learns a discriminative feature for person re-identification. This way, the domain gap between synthetic and real-world data is much reduced, and the learned feature is generalizable thanks to the large-scale and diverse training data. Experimental results show that the proposed annotation-free method is more or less comparable to the counterpart trained with full human annotations, which is quite promising. In addition, it achieves the current state of the art on several person re-identification datasets under direct cross-dataset evaluation.
Although existing person re-identification (Re-ID) methods have shown impressive accuracy, most of them usually suffer from poor generalization on unseen target domain. Thus, generalizable person Re-ID has recently drawn increasing attention, which t
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