2 research outputs found
Multi-Domain Adversarial Feature Generalization for Person Re-Identification
With the assistance of sophisticated training methods applied to single
labeled datasets, the performance of fully-supervised person re-identification
(Person Re-ID) has been improved significantly in recent years. However, these
models trained on a single dataset usually suffer from considerable performance
degradation when applied to videos of a different camera network. To make
Person Re-ID systems more practical and scalable, several cross-dataset domain
adaptation methods have been proposed, which achieve high performance without
the labeled data from the target domain. However, these approaches still
require the unlabeled data of the target domain during the training process,
making them impractical. A practical Person Re-ID system pre-trained on other
datasets should start running immediately after deployment on a new site
without having to wait until sufficient images or videos are collected and the
pre-trained model is tuned. To serve this purpose, in this paper, we
reformulate person re-identification as a multi-dataset domain generalization
problem. We propose a multi-dataset feature generalization network (MMFA-AAE),
which is capable of learning a universal domain-invariant feature
representation from multiple labeled datasets and generalizing it to `unseen'
camera systems. The network is based on an adversarial auto-encoder to learn a
generalized domain-invariant latent feature representation with the Maximum
Mean Discrepancy (MMD) measure to align the distributions across multiple
domains. Extensive experiments demonstrate the effectiveness of the proposed
method. Our MMFA-AAE approach not only outperforms most of the domain
generalization Person Re-ID methods, but also surpasses many state-of-the-art
supervised methods and unsupervised domain adaptation methods by a large
margin.Comment: TIP (Accept with Mandatory Minor Revisions
End-to-end correspondence and relationship learning of mid-level deep features for person re-identification
In this paper, a unified deep convolutional architecture is proposed to address the problems in the person re-identification task. The proposed method adaptively learns the discriminative deep mid-level features of a person and constructs the correspondence features between an image pair in a data-driven manner. The previous Siamese structure deep learning approaches focus only on pair-wise matching between features. In our method, we consider the latent relationship between mid-level features and propose a network structure to automatically construct the correspondence features from all input features without a pre-defined matching function. The experimental results on three benchmarks VIPeR, CUHK01 and CUHK03 show that our unified approach improves over the previous state-of-the-art methods