468 research outputs found
Dual Clustering Co-teaching with Consistent Sample Mining for Unsupervised Person Re-Identification
In unsupervised person Re-ID, peer-teaching strategy leveraging two networks
to facilitate training has been proven to be an effective method to deal with
the pseudo label noise. However, training two networks with a set of noisy
pseudo labels reduces the complementarity of the two networks and results in
label noise accumulation. To handle this issue, this paper proposes a novel
Dual Clustering Co-teaching (DCCT) approach. DCCT mainly exploits the features
extracted by two networks to generate two sets of pseudo labels separately by
clustering with different parameters. Each network is trained with the pseudo
labels generated by its peer network, which can increase the complementarity of
the two networks to reduce the impact of noises. Furthermore, we propose dual
clustering with dynamic parameters (DCDP) to make the network adaptive and
robust to dynamically changing clustering parameters. Moreover, Consistent
Sample Mining (CSM) is proposed to find the samples with unchanged pseudo
labels during training for potential noisy sample removal. Extensive
experiments demonstrate the effectiveness of the proposed method, which
outperforms the state-of-the-art unsupervised person Re-ID methods by a
considerable margin and surpasses most methods utilizing camera information
Camera-aware Proxies for Unsupervised Person Re-Identification
This paper tackles the purely unsupervised person re-identification (Re-ID)
problem that requires no annotations. Some previous methods adopt clustering
techniques to generate pseudo labels and use the produced labels to train Re-ID
models progressively. These methods are relatively simple but effective.
However, most clustering-based methods take each cluster as a pseudo identity
class, neglecting the large intra-ID variance caused mainly by the change of
camera views. To address this issue, we propose to split each single cluster
into multiple proxies and each proxy represents the instances coming from the
same camera. These camera-aware proxies enable us to deal with large intra-ID
variance and generate more reliable pseudo labels for learning. Based on the
camera-aware proxies, we design both intra- and inter-camera contrastive
learning components for our Re-ID model to effectively learn the ID
discrimination ability within and across cameras. Meanwhile, a proxy-balanced
sampling strategy is also designed, which facilitates our learning further.
Extensive experiments on three large-scale Re-ID datasets show that our
proposed approach outperforms most unsupervised methods by a significant
margin. Especially, on the challenging MSMT17 dataset, we gain Rank-1
and mAP improvements when compared to the second place. Code is
available at: \texttt{https://github.com/Terminator8758/CAP-master}.Comment: Accepted to AAAI 2021. Code is available at:
https://github.com/Terminator8758/CAP-maste
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
Recent self-supervised contrastive learning provides an effective approach
for unsupervised person re-identification (ReID) by learning invariance from
different views (transformed versions) of an input. In this paper, we
incorporate a Generative Adversarial Network (GAN) and a contrastive learning
module into one joint training framework. While the GAN provides online data
augmentation for contrastive learning, the contrastive module learns
view-invariant features for generation. In this context, we propose a
mesh-based view generator. Specifically, mesh projections serve as references
towards generating novel views of a person. In addition, we propose a
view-invariant loss to facilitate contrastive learning between original and
generated views. Deviating from previous GAN-based unsupervised ReID methods
involving domain adaptation, we do not rely on a labeled source dataset, which
makes our method more flexible. Extensive experimental results show that our
method significantly outperforms state-of-the-art methods under both, fully
unsupervised and unsupervised domain adaptive settings on several large scale
ReID datsets.Comment: CVPR 2021. Source code: https://github.com/chenhao2345/GC
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
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