23 research outputs found
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Person re-identification (re-ID) aims at recognizing the same person from
images taken across different cameras. To address this challenging task,
existing re-ID models typically rely on a large amount of labeled training
data, which is not practical for real-world applications. To alleviate this
limitation, researchers now targets at cross-dataset re-ID which focuses on
generalizing the discriminative ability to the unlabeled target domain when
given a labeled source domain dataset. To achieve this goal, our proposed Pose
Disentanglement and Adaptation Network (PDA-Net) aims at learning deep image
representation with pose and domain information properly disentangled. With the
learned cross-domain pose invariant feature space, our proposed PDA-Net is able
to perform pose disentanglement across domains without supervision in
identities, and the resulting features can be applied to cross-dataset re-ID.
Both of our qualitative and quantitative results on two benchmark datasets
confirm the effectiveness of our approach and its superiority over the
state-of-the-art cross-dataset Re-ID approaches.Comment: Accepted to ICCV 201
Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification
While recent person re-identification (ReID) methods achieve high accuracy in
a supervised setting, their generalization to an unlabelled domain is still an
open problem. In this paper, we introduce a novel unsupervised disentanglement
generative adversarial network (UD-GAN) to address the domain adaptation issue
of supervised person ReID. Our framework jointly trains a ReID network for
discriminative features extraction in a source labelled domain using identity
annotation, and adapts the ReID model to an unlabelled target domain by
learning disentangled latent representations on the domain. Identity-unrelated
features in the target domain are distilled from the latent features. As a
result, the ReID features better encompass the identity of a person in the
unsupervised domain. We conducted experiments on the Market1501, DukeMTMC and
MSMT17 datasets. Results show that the unsupervised domain adaptation problem
in ReID is very challenging. Nevertheless, our method shows improvement in half
of the domain transfers and achieve state-of-the-art performance for one of
them.Comment: 8 pages, 5 figures, submitted to ICPR 202
Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification
Person re-identification (ReID) remains a challenging task in many real-word
video analytics and surveillance applications, even though state-of-the-art
accuracy has improved considerably with the advent of deep learning (DL) models
trained on large image datasets. Given the shift in distributions that
typically occurs between video data captured from the source and target
domains, and absence of labeled data from the target domain, it is difficult to
adapt a DL model for accurate recognition of target data. We argue that for
pair-wise matchers that rely on metric learning, e.g., Siamese networks for
person ReID, the unsupervised domain adaptation (UDA) objective should consist
in aligning pair-wise dissimilarity between domains, rather than aligning
feature representations. Moreover, dissimilarity representations are more
suitable for designing open-set ReID systems, where identities differ in the
source and target domains. In this paper, we propose a novel
Dissimilarity-based Maximum Mean Discrepancy (D-MMD) loss for aligning
pair-wise distances that can be optimized via gradient descent. From a person
ReID perspective, the evaluation of D-MMD loss is straightforward since the
tracklet information allows to label a distance vector as being either
within-class or between-class. This allows approximating the underlying
distribution of target pair-wise distances for D-MMD loss optimization, and
accordingly align source and target distance distributions. Empirical results
with three challenging benchmark datasets show that the proposed D-MMD loss
decreases as source and domain distributions become more similar. Extensive
experimental evaluation also indicates that UDA methods that rely on the D-MMD
loss can significantly outperform baseline and state-of-the-art UDA methods for
person ReID without the common requirement for data augmentation and/or complex
networks.Comment: 14 pages (16 pages with references), 7 figures, conference ECC
Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification
Many unsupervised domain adaptive (UDA) person re-identification (ReID)
approaches combine clustering-based pseudo-label prediction with feature
fine-tuning. However, because of domain gap, the pseudo-labels are not always
reliable and there are noisy/incorrect labels. This would mislead the feature
representation learning and deteriorate the performance. In this paper, we
propose to estimate and exploit the credibility of the assigned pseudo-label of
each sample to alleviate the influence of noisy labels, by suppressing the
contribution of noisy samples. We build our baseline framework using the mean
teacher method together with an additional contrastive loss. We have observed
that a sample with a wrong pseudo-label through clustering in general has a
weaker consistency between the output of the mean teacher model and the student
model. Based on this finding, we propose to exploit the uncertainty (measured
by consistency levels) to evaluate the reliability of the pseudo-label of a
sample and incorporate the uncertainty to re-weight its contribution within
various ReID losses, including the identity (ID) classification loss per
sample, the triplet loss, and the contrastive loss. Our uncertainty-guided
optimization brings significant improvement and achieves the state-of-the-art
performance on benchmark datasets.Comment: 9 pages. Accepted to 35th AAAI Conference on Artificial Intelligence
(AAAI 2021
Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
Unsupervised domain adaptation (UDA) aims at adapting the model trained on a
labeled source-domain dataset to an unlabeled target-domain dataset. The task
of UDA on open-set person re-identification (re-ID) is even more challenging as
the identities (classes) do not overlap between the two domains. One major
research direction was based on domain translation, which, however, has fallen
out of favor in recent years due to inferior performance compared to
pseudo-label-based methods. We argue that translation-based methods have great
potential on exploiting the valuable source-domain data but they did not
provide proper regularization on the translation process. Specifically, these
methods only focus on maintaining the identities of the translated images while
ignoring the inter-sample relation during translation. To tackle the challenge,
we propose an end-to-end structured domain adaptation framework with an online
relation-consistency regularization term. During training, the person feature
encoder is optimized to model inter-sample relations on-the-fly for supervising
relation-consistency domain translation, which in turn, improves the encoder
with informative translated images. An improved pseudo-label-based encoder can
therefore be obtained by jointly training the source-to-target translated
images with ground-truth identities and target-domain images with pseudo
identities. In the experiments, our proposed framework is shown to outperform
state-of-the-art methods on multiple UDA tasks of person re-ID. Code is
available at https://github.com/yxgeee/SDA