45 research outputs found
Unsupervised Person Re-identification by Soft Multilabel Learning
Although unsupervised person re-identification (RE-ID) has drawn increasing
research attentions due to its potential to address the scalability problem of
supervised RE-ID models, it is very challenging to learn discriminative
information in the absence of pairwise labels across disjoint camera views. To
overcome this problem, we propose a deep model for the soft multilabel learning
for unsupervised RE-ID. The idea is to learn a soft multilabel (real-valued
label likelihood vector) for each unlabeled person by comparing (and
representing) the unlabeled person with a set of known reference persons from
an auxiliary domain. We propose the soft multilabel-guided hard negative mining
to learn a discriminative embedding for the unlabeled target domain by
exploring the similarity consistency of the visual features and the soft
multilabels of unlabeled target pairs. Since most target pairs are cross-view
pairs, we develop the cross-view consistent soft multilabel learning to achieve
the learning goal that the soft multilabels are consistently good across
different camera views. To enable effecient soft multilabel learning, we
introduce the reference agent learning to represent each reference person by a
reference agent in a joint embedding. We evaluate our unified deep model on
Market-1501 and DukeMTMC-reID. Our model outperforms the state-of-the-art
unsupervised RE-ID methods by clear margins. Code is available at
https://github.com/KovenYu/MAR.Comment: CVPR19, ora
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