30 research outputs found
Bi-Directional Generation for Unsupervised Domain Adaptation
Unsupervised domain adaptation facilitates the unlabeled target domain
relying on well-established source domain information. The conventional methods
forcefully reducing the domain discrepancy in the latent space will result in
the destruction of intrinsic data structure. To balance the mitigation of
domain gap and the preservation of the inherent structure, we propose a
Bi-Directional Generation domain adaptation model with consistent classifiers
interpolating two intermediate domains to bridge source and target domains.
Specifically, two cross-domain generators are employed to synthesize one domain
conditioned on the other. The performance of our proposed method can be further
enhanced by the consistent classifiers and the cross-domain alignment
constraints. We also design two classifiers which are jointly optimized to
maximize the consistency on target sample prediction. Extensive experiments
verify that our proposed model outperforms the state-of-the-art on standard
cross domain visual benchmarks.Comment: 9 pages, 4 figure
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification
Unsupervised domain adaptive person Re-IDentification (ReID) is challenging
because of the large domain gap between source and target domains, as well as
the lackage of labeled data on the target domain. This paper tackles this
challenge through jointly enforcing visual and temporal consistency in the
combination of a local one-hot classification and a global multi-class
classification. The local one-hot classification assigns images in a training
batch with different person IDs, then adopts a Self-Adaptive Classification
(SAC) model to classify them. The global multi-class classification is achieved
by predicting labels on the entire unlabeled training set with the Memory-based
Temporal-guided Cluster (MTC). MTC predicts multi-class labels by considering
both visual similarity and temporal consistency to ensure the quality of label
prediction. The two classification models are combined in a unified framework,
which effectively leverages the unlabeled data for discriminative feature
learning. Experimental results on three large-scale ReID datasets demonstrate
the superiority of proposed method in both unsupervised and unsupervised domain
adaptive ReID tasks. For example, under unsupervised setting, our method
outperforms recent unsupervised domain adaptive methods, which leverage more
labels for training