5,158 research outputs found
Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification
In this paper, we focus on the semi-supervised person re-identification
(Re-ID) case, which only has the intra-camera (within-camera) labels but not
inter-camera (cross-camera) labels. In real-world applications, these
intra-camera labels can be readily captured by tracking algorithms or few
manual annotations, when compared with cross-camera labels. In this case, it is
very difficult to explore the relationships between cross-camera persons in the
training stage due to the lack of cross-camera label information. To deal with
this issue, we propose a novel Progressive Cross-camera Soft-label Learning
(PCSL) framework for the semi-supervised person Re-ID task, which can generate
cross-camera soft-labels and utilize them to optimize the network. Concretely,
we calculate an affinity matrix based on person-level features and adapt them
to produce the similarities between cross-camera persons (i.e., cross-camera
soft-labels). To exploit these soft-labels to train the network, we investigate
the weighted cross-entropy loss and the weighted triplet loss from the
classification and discrimination perspectives, respectively. Particularly, the
proposed framework alternately generates progressive cross-camera soft-labels
and gradually improves feature representations in the whole learning course.
Extensive experiments on five large-scale benchmark datasets show that PCSL
significantly outperforms the state-of-the-art unsupervised methods that employ
labeled source domains or the images generated by the GAN-based models.
Furthermore, the proposed method even has a competitive performance with
respect to deep supervised Re-ID methods.Comment: Accepted by IEEE Transactions on Circuits and Systems for Video
Technology (TCSVT
ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification
Unsupervised person re-identification (ReID) aims at learning discriminative
identity features without annotations. Recently, self-supervised contrastive
learning has gained increasing attention for its effectiveness in unsupervised
representation learning. The main idea of instance contrastive learning is to
match a same instance in different augmented views. However, the relationship
between different instances of a same identity has not been explored in
previous methods, leading to sub-optimal ReID performance. To address this
issue, we propose Inter-instance Contrastive Encoding (ICE) that leverages
inter-instance pairwise similarity scores to boost previous class-level
contrastive ReID methods. We first use pairwise similarity ranking as one-hot
hard pseudo labels for hard instance contrast, which aims at reducing
intra-class variance. Then, we use similarity scores as soft pseudo labels to
enhance the consistency between augmented and original views, which makes our
model more robust to augmentation perturbations. Experiments on several
large-scale person ReID datasets validate the effectiveness of our proposed
unsupervised method ICE, which is competitive with even supervised methods
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
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