# Progressive Contrastive Learning with Multi-Prototype for Unsupervised
Visible-Infrared Person Re-identification

Jiangming Shi1, Xiangbo Yin2∗, Yaoxing Wang3, Xiaofeng Liu4
Yuan Xie5†, Yanyun Qu1,2
1 Institute of Artificial Intelligence, 2 School of Informatics, Xiamen
University
3 Ningxia University, 4 Hohai University, 5 East China Normal University
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Equal
contribution.Corresponding author.

###### Abstract

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to
match specified people in infrared images to visible images without
annotation, and vice versa. USVI-ReID is a challenging yet under-explored
task. Most existing methods address the USVI-ReID problem using cluster-based
contrastive learning, which simply employs the cluster center as a
representation of a person. However, the cluster center primarily focuses on
shared information, overlooking disparity. To address the problem, we propose
a Progressive Contrastive Learning with Multi-Prototype (PCLMP) method for
USVI-ReID. In brief, we first generate the hard prototype by selecting the
sample with the maximum distance from the cluster center. This hard prototype
is used in the contrastive loss to emphasize disparity. Additionally, instead
of rigidly aligning query images to a specific prototype, we generate the
dynamic prototype by randomly picking samples within a cluster. This dynamic
prototype is used to retain the natural variety of features while reducing
instability in the simultaneous learning of both common and disparate
information. Finally, we introduce a progressive learning strategy to
gradually shift the model’s attention towards hard samples, avoiding cluster
deterioration. Extensive experiments conducted on the publicly available SYSU-
MM01 and RegDB datasets validate the effectiveness of the proposed method.
PCLMP outperforms the existing state-of-the-art method with an average mAP
improvement of 3.9%. The source codes will be released.

##  1 Introduction

Figure 1:  Illustration for the core idea of our proposal. Left: Initial
sample distribution and movement trends during optimization. Right: Sample
distribution upon completion of optimization. CPCL denotes Centroid Prototype
Contrastive Learning, HPCL for Hard Prototype Contrastive Learning, and DPCL
for Dynamic Prototype Contrastive Learning.

Visible-infrared person re-identification (VI-ReID) aims at matching the same
person captured in one modality with their counterparts in another modality Ye
et al. (2021); Gong et al. (2024); Yang et al. (2024). With the development of
deep learning, VI-ReID has achieved remarkable advancements Yu et al. (2023);
Kim et al. (2023); Feng et al. (2023); Fang et al. (2023). However, the
development of existing VI-ReID methods is still limited due to the
requirement for expensive-annotated training data Pang et al. (2022). To
mitigate the problem of annotating large-scale cross-modality data, some semi-
supervised VI-ReID methods Wang et al. (2022); Shi et al. (2023); Yang et al.
(2023b) are proposed to learn modality-invariant and identity-related
discriminative representations by utilizing both labeled and unlabeled data.
For this purpose, OTLA Wang et al. (2022) proposed an optimal-transport label
assignment mechanism to assign pseudo-labels for unlabeled infrared images
while ignoring how to calibrate noise pseudo-labels. DPIS Shi et al. (2023)
integrates two pseudo-labels generated by distinct models into a hybrid
pseudo-label for unlabeled infrared data, but it makes the training process
more complex. Although these methods have gained promising performances, they
still rely on a certain number of manual-labeled data.

USVI-ReID is gaining prominence and several methods Yang et al. (2022a); Zhang
et al. (2023b); Yang et al. (2023c); Wu and Ye (2023) have proposed to tackle
the issues of expensive visible-infrared annotation through contrastive
learning. These methods create two modality-specific memories, one for visible
features and the other for infrared features. During training, these methods
consider the memory center as a centroid prototype and minimize the
contrastive loss across the features of query images and centroid prototype.
Then, these methods aggregate the corresponding centroid prototypes based on
similarity. However, the centroid prototype only stores the common information
of each person, neglecting the diverse information. Just like a normal
distribution, to better reflect the data distribution of a dataset, we need
not only the mean but also the variance. Additionally, contrastive loss based
on the centroid prototype may blur the boundaries across different clusters as
illustrated in the top row of Fig. 1.

In this paper, we rethink the design of the prototype used in contrastive
learning for USVI-ReID. Firstly, we design a Hard Prototype Contrastive
Learning (HPCL) to mine diverse yet meaningful information and improve the
boundaries across different clusters. In contrast to traditional contrastive
learning methods, we choose the hard samples to serve as the hard prototype as
shown in the middle row of Fig. 1. In other words, the hard prototype is the
one that is farthest from the memory center. The hard prototype encompasses
distinctive information. Furthermore, we introduce the concept of Dynamic
Prototype Contrastive Learning (DPCL), we randomly select samples from each
cluster to serve as the dynamic prototype. DPCL effectively accounts for the
intrinsic diversity within clusters, enhancing the model’s adaptability to
varying data distributions. The dynamic prototype offers more comprehensive
representations and relationships compared with memory samples overlapping, as
shown in the bottom low of Fig. 1. Early clustering results are unreliable,
and utilizing hard samples at this stage may lead to cluster degradation.
Therefore, we introduce progressive contrastive learning to gradually focus on
hard samples.

The main contributions are summarized as follows:

  * •

We propose a progressive contrastive learning with multi-prototype method for
the USVI-ReID. We reconsider the design of prototypes in progressive
contrastive learning to ensure that the model stably captures both commonality
and diversitiy.

  * •

We propose Hard Prototype Contrastive Learning for mining distinctive yet
significant information, and Dynamic Prototype Contrastive Learning for
preserving the intrinsic diversity in sample features.

  * •

The experimental results on the SYSU-MM01 and RegDB datasets with superior
performance against the state-of-the-art methods demonstrate the effectiveness
of the proposed method.

##  2 Related Work

###  2.1 Supervised Visible-Infrared Person ReID

Visible-Infrared Person ReID (VI-ReID) has drawn much attention in recent
years Ye et al. (2021); Yang et al. (2022b). Many VI-ReID works focused on
mitigating huge semantic gaps across modalities have made advanced progress,
which can be classified into two primary classes based on their different
aligning ways: image-level alignment and feature-level alignment. The image-
level alignment methods focus on reducing cross-modality gaps by modality
translation. Some GAN-based methods Wang et al. (2020, 2019) are proposed to
perform style transformation for aligning cross-modality images. However, the
generated images unavoidably contain noise. Therefore, X-modality Li et al.
(2020) and its promotion Zhang et al. (2021); Wei et al. (2021) align cross-
modality images by introducing a middle modality. Mainstream feature-level
alignment methods Liu et al. (2022); Zhang et al. (2022); Ye et al. (2020);
Sun et al. (2022); Wu et al. (2021) focus on minimizing cross-modality gaps by
finding a modality-shared feature space. To this end, SAAI Fang et al. (2023)
proposes a Semantic Alignment and Affinity Inference framework, which aligns
the latent semantic part features by shared prototypes, enabling the
aggregation of latent semantic part features with the same semantic
information across both modalities. However, the advanced performances of the
above methods build on large-scale human-labeled cross-modality data, which
are quite time-consuming and expensive, thus hindering the fast application of
these methods in real-scenes.

Figure 2: Framework of our PCLMP. The framework consists of two stages: the
first stage employs contrastive learning with centroid prototypes to learn
well-discriminative representation, and the second stage introduces
contrastive learning with hard and dynamic prototypes to further focus on hard
samples.

###  2.2 Unsupervised Single-Modality Person ReID

The existing unsupervised single-modality person ReID methods can be roughly
divided into two classes: Unsupervised domain adaption methods (UDA), which
try to leverage the knowledge transferred from labeled source domain to
improve performance Ge et al. (2020a, b), and Purely unsupervised methods
(USL), which directly train a USL-ReID model on the unlabeled target domain
Zhang et al. (2023b); Cho et al. (2022). Compared with the UDA methods, the
USL methods are more challenging. Recently, cluster-contrast learning Dai et
al. (2022) has achieved impressive performance by performing contrastive
learning at the cluster level. However, cluster-contrast with a uni-proxy can
be biased and confusing, which fails to accurately describe the information of
a cluster. To this end, methods Zou et al. (2023); Wu et al. (2022) proposed
maintaining multi-proxies for a cluster to adaptively capture different
information within the cluster. The above methods are mainly proposed to solve
the single-modality ReID task, but they are limited to solving the USL-VI-ReID
task due to large cross-modality gaps.

###  2.3 Unsupervised Visible-Infrared Person ReID

Recently, unsupervised visible-infrared person ReID has attracted much
attention due to the advantage of not relying on any data annotation. Some UDA
methods Liang et al. (2021); Wang et al. (2022) use a well-annotated labeled
source domain for pre-training to solve the USVI-ReID task. Some purely
unsupervised methods Yang et al. (2022a); Wu and Ye (2023); Yang et al.
(2023c, a) adopt contrastive learning to boost performance inspired by Dai et
al. (2022), which mainly follow a two-step loop paradigm: generating pseudo-
labels using the DBSCAN algorithm Ester et al. (1996) to create memory banks
with clustering centers and establishing cross-modality correspondences based
on these memory banks. However, pseudo-labels are often inaccurate and rigid,
CCLNet Chen et al. (2023) leverages the text information from CLIP to afford
greater semantic monitoring insights to compensate for the rigidity of pseudo-
labels. Moreover, reliable cross-modality correspondences are vital to USVI-
ReID, thus PGM Wu and Ye (2023) proposes a progressive graph matching
framework to establish more reliable cross-modality correspondences. However,
cluster centers mainly present common information while lacking distinctive
information, which results in ambiguous cross-modality correspondences when
meeting hard samplesYang et al. (2021, 2023d)

##  3 Method

###  3.1 Overview and Problem Formulation

We propose a Progressive Contrastive Learning with Multi-Prototype (PCLMP)
method for USVI-ReID, which mainly contains an online backbone, momentum
backbone, and progressive contrastive learning strategy with a centroid
prototype, a hard prototype, and a dynamic prototype, as shown in Fig. 2. The
online backbone is a standard network, updated through back-propagation. The
momentum backbone mirrors the structure of the online backbone, updated
through the weights of the online backbone. The clustering is used to generate
pseudo labels for creating cluster-aware memory, and we employ DBSCAN Ester et
al. (1996) for this clustering. We use progressive contrastive learning with
multi-prototype to optimize the online backbone. PCLMP primarily focuses on
representation learning, and we use PGM Wu and Ye (2023) to aggregate cross-
modality memory.

Given a USVI-ReID dataset $D=\\{V,R\\}$, where $V=\\{V_{i}\\}^{N_{v}}_{i=1}$
represents the visible images and $R=\\{R_{j}\\}^{N_{r}}_{j=1}$ denotes the
infrared images. $V_{i}$ and $R_{j}$ represent the set of images corresponding
to the $i$-th and $j$-th class. $N_{v}$ and $N_{r}$ denote the number of
visible and infrared clusters, respectively. $q^{v}$ and $q^{r}$ are visible
and infrared query instance features. In the USVI-ReID task, the purpose is to
train a deep neural network to obtain modality-invariant and identity-related
features from images without any annotation.

###  3.2 Centroid Prototype Contrastive Learning

Following the USVI-ReID methods Yang et al. (2022a, 2023c); Shi et al. (2024),
our method uses centroid prototype contrastive learning to optimize the online
backbone $\phi_{0}$ in the first state, which includes memory initialization
and optimization.

Memory Initialization. At the beginning of each training epoch, all image
features are clustered by DBSCAN Ester et al. (1996) and then each cluster’s
representations are stored in visible memory $M_{RGB}=${$m^{v}_{1}$,
$m^{v}_{2}$, $\cdots$, $m^{v}_{N_{v}}$} and infrared memory
$M_{IR}$={$m^{r}_{1}$, $m^{r}_{2}$, $\cdots$, $m^{r}_{N_{r}}$}, as follows:

$m^{v}_{i}=\frac{1}{|V_{i}|}\sum_{v\in V_{i}}\phi_{0}(v),$ (1)
$m^{r}_{j}=\frac{1}{|R_{j}|}\sum_{r\in R_{j}}\phi_{0}(r),$ (2)

where $|\cdot|$ denotes the number of instances.

Optimization. During training, we update the two modality-specific memories by
a momentum updating strategy Dai et al. (2022). We treat the memory center as
a centroid prototype and optimize the feature extractor $\phi_{0}$ using
contrastive learning with the centroid prototype, computed as:

$L^{v}_{CPCL}=-\log\frac{\exp\left(q^{v}\cdot{m_{+}^{v}}/\tau\right)}{\sum_{i=1}^{N_{v}}\exp\left(q^{v}\cdot{m_{i}^{v}}/\tau\right)},$
(3)

$L^{r}_{CPCL}=-\log\frac{\exp\left(q^{r}\cdot{m_{+}^{r}}/\tau\right)}{\sum_{j=1}^{N_{r}}\exp\left(q^{r}\cdot{m_{j}^{r}}/\tau\right)},$
(4) $L_{CPCL}=L^{v}_{CPCL}+L^{r}_{CPCL},$ (5)

where $m_{+}^{v(r)}$ is the positive centroid prototype, denoting a query and
the prototype shares the same pseudo label. The $\tau$ is a temperature hyper-
parameter.

###  3.3 Hard Prototype Contrastive Learning

To ensure that the prototype effectively captures both the commonality and
variation in a person, we devise a novel prototype for contrastive learning,
which is referred to as Hard Prototype Contrastive Learning. HPCL is designed
to provide a comprehensive understanding of personal characteristics, which
benefits its handling of hard samples.

We use the online backbone $\phi_{0}$ to extract feature representations, and
select $k$ samples that are farthest from the memory center as the hard
prototype:

$hm^{v}_{i}=\underset{{{\forall v\in
V_{i}}}}{\arg\max}\left\|\phi_{0}(v)-m^{v}_{i}\right\|,$ (6)

$hm^{r}_{j}=\underset{{{\forall r\in
R_{j}}}}{\arg\max}\left\|\phi_{0}(r)-m^{r}_{j}\right\|.$ (7)

Then, we construct contrastive loss with the hard prototype to minimize the
distance between the query and the positive hard prototype while maximizing
their discrepancy to all other cluster hard prototypes, as follows:

$L_{HPCL}^{v}=-\log\frac{\exp\left(q^{v}\cdot{hm_{+}^{v}}/\tau\right)}{\sum_{i=1}^{N_{v}}\exp\left(q^{v}\cdot{hm_{i}^{v}}/\tau\right)},$
(8)

$L_{HPCL}^{r}=-\log\frac{\exp\left(q^{r}\cdot{hm_{+}^{r}}/\tau\right)}{\sum_{j=1}^{N_{r}}\exp\left(q^{r}\cdot{hm_{j}^{r}}/\tau\right)},$
(9)

$L_{HPCL}=L_{HPCL}^{v}+L_{HPCL}^{r},$ (10)

where $hm_{+}^{v(r)}$ is the positive hard prototype representation and the
$\tau$ is a temperature hyper-parameter.

Finally, we update the two modality-specific memories with a momentum-updating
strategy:

$hm^{v}_{+(t)}=\alpha{hm}^{v}_{+(t-1)}+(1-\alpha)q^{v}_{t},$ (11)
$hm^{r}_{+(t)}=\alpha{hm}^{r}_{+(t-1)}+(1-\alpha)q^{r}_{t},$ (12)

where $\alpha$ is a momentum coefficient that controls the update speed of the
memories. $t$ and $t-1$ refer to the current and last iteration, respectively.

The hard prototype contrastive learning has two main advantages: For intra-
class feature learning, it ensures that the learning process does not just
focus on the shared characteristics within a cluster but also considers the
diverse elements, which are often more informative. For inter-class feature
learning, it is also beneficial for increasing the distances between different
persons. In contrast, centroid prototypes tend to average features, lacking
diversity, which can affect the network’s ability to extract discriminative
features.

###  3.4 Dynamic Prototype Contrastive Learning

Inspired by MoCo He et al. (2020), we design dynamic prototype contrastive
learning in order to preserve the intrinsic diversity in sample features while
reducing instability in the simultaneous learning of both common and diverse
information. DPCL comprises an online backbone $\phi_{0}$ and a momentum
backbone $\phi_{m}$. The momentum backbone mirrors the structure of the online
backbone, which is updated by the accumulated weights of the online backbone:

${\phi}_{m}^{t}=\beta{\phi}_{m}^{t-1}+(1-\beta){\phi}_{0}^{t},$ (13)

where $\beta$ is a momentum coefficient that controls the update speed of the
momentum backbone, which is set to 0.999. $t$ and $t-1$ refer to the current
and last iteration, respectively. The momentum backbone ${\phi}_{m}$ update by
the moving averaged weights, which are more stable and resistant to sudden
fluctuations or noisy updates He et al. (2020).

We randomly select $M$ visible/infrared samples from each cluster, denoted as
$X^{v}_{i}$ and $X^{r}_{j}$. We use the momentum backbone $\phi_{m}$ to
extract feature representation and store them in visible memory
$DM_{RGB}$={$F^{v}_{1}$, $F^{v}_{2}$, $\cdots$, $F^{v}_{N_{v}}$} and infrared
memory $DM_{IR}$={$F^{r}_{1}$, $F^{r}_{2}$, $\cdots$, $F^{r}_{N_{r}}$}, as
follows:

$F^{v}_{i}=\phi_{m}(X^{v}_{i}),$ (14) $F^{r}_{j}=\phi_{m}(X^{r}_{j}).$ (15)

We select visible dynamic prototype $dm^{v}_{i}$ from $DM_{RGB}$. In the same
cluster, we select the sample farthest from the query image as the prototype.
In different clusters, we choose the sample closest to the query image as the
prototype:

$dm^{v}_{i}=\begin{cases}\underset{{{\forall f^{v}_{i}\in
F^{v}_{i}}}}{\arg\max}\left\|q^{v}-f^{v}_{i}\right\|&\text{if }y_{q}=y_{i}\\\
\underset{{{\forall f^{v}_{i}\in
F^{v}_{i}}}}{\arg\min}\left\|q^{v}-f^{v}_{i}\right\|&\text{if }y_{q}\neq
y_{i}\end{cases},$ (16)

where $y_{q}$ and $y_{i}$ represent the pseudo label of the query image and
the dynamic prototype, respectively. $\left\|\cdot\right\|$ denotes Euclidean
norm. We obtain infrared prototype $dm^{r}_{j}$ through the same method.

The overall optimization goal of DPCL is as follows:

$L_{DPCL}^{v}=-\log\frac{\exp\left(q^{v}\cdot{dm_{+}^{v}}/\tau\right)}{\sum_{i=1}^{N_{v}}\exp\left(q^{v}\cdot{dm_{i}^{v}}/\tau\right)},$
(17)

$L_{DPCL}^{r}=-\log\frac{\exp\left(q^{r}\cdot{dm_{+}^{r}}/\tau\right)}{\sum_{j=1}^{N_{r}}\exp\left(q^{r}\cdot{dm_{j}^{r}}/\tau\right)},$
(18) $L_{DPCL}=L_{DPCL}^{v}+L_{DPCL}^{r},$ (19)

where $dm_{+}^{v(r)}$ is the positive dynamic prototype representation, i.e.,
the query image and dynamic prototype have the same identity.

DPCL promotes a flexible and adaptable learning process, aiming to minimize
discrepancies between samples and their respective dynamic prototypes, rather
than rigidly aligning query images with a fixed prototype.

###  3.5 Progressive Contrastive Learning

In the initial training phases, representations are generally of lower
quality. Introducing hard samples at this period could be counterproductive,
potentially leading the model optimization in an incorrect direction right
from the start Zou et al. (2023); Gong et al. (2022). To address this issue,
we introduce the Progressive Contrastive Learning, which forms the overall
loss function:

${L}_{PCLMP}=\left\\{\begin{array}[]{l}{L}_{CPCL},\quad\text{ if }\text{ epoch
}\leqslant E_{\text{CPCL }}\\\
\lambda{L}_{HPCL}+(1-\lambda){L}_{DPCL},\quad\text{else}\end{array}\right.$
(20)

where $\lambda$ is the loss weight, $E_{\text{CPCL}}$ is a hyper-parameter.

Table 1: Comparisons with state-of-the-art methods on SYSU-MM01 and RegDB,
including SVI-ReID, SSVI-ReID and USVI-ReID methods. All methods are measured
by Rank-1 (%) and mAP (%). GUR* denotes the results without camera
information.

Settings | SYSU-MM01 | RegDB
---|---|---
All Search | Indoor Search | Visible2Thermal | Thermal2Visible
Type | Method | Venue | Rank-1 | mAP | Rank-1 | mAP | Rank-1 | mAP | Rank-1 | mAP
SVI-ReID |  DDAG Ye et al. (2020) |  ECCV’20 | 54.8 | 53.0 | 61.0 | 68.0 | 69.4 | 63.5 | 68.1 | 61.8
AGW Ye et al. (2022) |  TPAMI’21 | 47.5 | 47.7 | 54.2 | 63.0 | 70.1 | 66.4 | 70.5 | 65.9
NFS Chen et al. (2021) |  CVPR’21 | 56.9 | 55.5 | 62.8 | 69.8 | 80.5 | 72.1 | 78.0 | 69.8
LbA Park et al. (2021) |  ICCV’21 | 55.4 | 54.1 | 58.5 | 66.3 | 74.2 | 67.6 | 72.4 | 65.5
CAJ Ye et al. (2021) |  ICCV’21 | 69.9 | 66.9 | 76.3 | 80.4 | 85.0 | 79.1 | 84.8 | 77.8
MPANet Wu et al. (2021) |  CVPR’21 | 70.6 | 68.2 | 76.7 | 81.0 | 83.7 | 80.9 | 82.8 | 80.7
DART Yang et al. (2022b) |  CVPR’22 | 68.7 | 66.3 | 72.5 | 78.2 | 83.6 | 75.7 | 82.0 | 73.8
FMCNet Zhang et al. (2022) |  CVPR’22 | 66.3 | 62.5 | 68.2 | 74.1 | 89.1 | 84.4 | 88.4 | 83.9
MAUM Liu et al. (2022) |  CVPR’22 | 71.7 | 68.8 | 77.0 | 81.9 | 87.9 | 85.1 | 87.0 | 84.3
MID Huang et al. (2022) |  AAAI’22 | 60.3 | 59.4 | 64.9 | 70.1 | 87.5 | 84.9 | 84.3 | 81.4
LUPI Alehdaghi et al. (2022) |  ECCV’22 | 71.1 | 67.6 | 82.4 | 82.7 | 88.0 | 82.7 | 86.8 | 81.3
PMT Lu et al. (2023) |  AAAI’23 | 67.5 | 65.0 | 71.7 | 76.5 | 84.8 | 76.7 | 84.2 | 75.1
DEEN Zhang and Wang (2023) |  CVPR’23 | 74.7 | 71.8 | 80.3 | 83.3 | 91.1 | 85.1 | 89.5 | 83.4
SGIEL Feng et al. (2023) |  CVPR’23 | 77.1 | 72.3 | 82.1 | 83.0 | 92.2 | 86.6 | 91.1 | 85.2
PartMix Kim et al. (2023) |  CVPR’23 | 77.8 | 74.6 | 81.5 | 84.4 | 85.7 | 82.3 | 84.9 | 82.5
ProtoHPE Zhang et al. (2023a) |  MM’23 | 71.9 | 70.6 | 77.8 | 81.3 | 88.7 | 83.7 | 88.7 | 82.0
CAL Wu et al. (2023) |  ICCV’23 | 74.7 | 71.7 | 79.7 | 83.7 | 94.5 | 88.7 | 93.6 | 87.6
MUN Yu et al. (2023) |  ICCV’23 | 76.2 | 73.8 | 79.4 | 82.1 | 95.2 | 87.2 | 91.9 | 85.0
SAAI Fang et al. (2023) |  ICCV’23 | 75.9 | 77.0 | 83.2 | 88.0 | 91.1 | 91.5 | 92.1 | 92.0
FDNM Zhang et al. (2024) |  arXiv’24 | 77.8 | 75.1 | 87.3 | 89.1 | 95.5 | 90.0 | 94.0 | 88.7
SSVI-ReID |  MAUM-50 Liu et al. (2022) |  CVPR’22 | 28.8 | 36.1 | - | - | - | - | - | -
MAUM-100 Liu et al. (2022) |  CVPR’22 | 38.5 | 39.2 | - | - | - | - | - | -
OTLA Wang et al. (2022) |  ECCV’22 | 48.2 | 43.9 | 47.4 | 56.8 | 49.9 | 41.8 | 49.6 | 42.8
TAA Yang et al. (2023b) |  TIP’23 | 48.8 | 42.3 | 50.1 | 56.0 | 62.2 | 56.0 | 63.8 | 56.5
DPIS Shi et al. (2023) |  ICCV’23 | 58.4 | 55.6 | 63.0 | 70.0 | 62.3 | 53.2 | 61.5 | 52.7
USVI-ReID |  H2H Liang et al. (2021) |  TIP’21 | 30.2 | 29.4 | - | - | 23.8 | 18.9 | - | -
OTLA Wang et al. (2022) |  ECCV’22 | 29.9 | 27.1 | 29.8 | 38.8 | 32.9 | 29.7 | 32.1 | 28.6
ADCA Yang et al. (2022a) |  MM’22 | 45.5 | 42.7 | 50.6 | 59.1 | 67.2 | 64.1 | 68.5 | 63.8
NGLR Cheng et al. (2023) |  MM’23 | 50.4 | 47.4 | 53.5 | 61.7 | 85.6 | 76.7 | 82.9 | 75.0
MBCCM He et al. (2023) |  MM’23 | 53.1 | 48.2 | 55.2 | 62.0 | 83.8 | 77.9 | 82.8 | 76.7
CCLNet Chen et al. (2023) |  MM’23 | 54.0 | 50.2 | 56.7 | 65.1 | 69.9 | 65.5 | 70.2 | 66.7
PGM Wu and Ye (2023) |  CVPR’23 | 57.3 | 51.8 | 56.2 | 62.7 | 69.5 | 65.4 | 69.9 | 65.2
GUR* Yang et al. (2023c) |  ICCV’23 | 61.0 | 57.0 | 64.2 | 69.5 | 73.9 | 70.2 | 75.0 | 69.9
MMM Shi et al. (2024) |  arXiv’24 | 61.6 | 57.9 | 64.4 | 70.4 | 89.7 | 80.5 | 85.8 | 77.0
| PCLMP | Ours | 64.4 | 58.7 | 69.5 | 74.4 | 84.3 | 80.7 | 82.7 | 78.4
| PCLMP+MMM | Enhanced | 65.9 | 61.8 | 70.3 | 74.9 | 89.6 | 83.7 | 87.0 | 80.9

##  4 Experiment

We conduct extensive experiments to validate the superiority of our proposed
method. First, we provide the detailed experiment setting, which contains
datasets, evaluation protocols, and implementation details. Then, we compare
our method with many state-of-the-art VI-ReID methods and conduct ablation
studies. In addition, to better illustrate our method, we also exhibit further
analysis. If not specified, we conduct analysis experiments on SYSU-MM01 in
the single-shot & all-search mode.

Table 2: Ablation studies on SYSU-MM01 in all search mode and indoor search
mode. “Baseline” means the model trained following PGM Wu and Ye (2023).
Rank-R accuracy(%) and mAP(%) are reported.

| Method | All Search | Indoor Search
---|---|---|---
Index | Baseline | HPCL | DPCL | PCL | Rank-1 | mAP | Rank-1 | mAP
1 | ✓ |  |  |  | 56.3 | 51.7 | 60.5 | 66.2
2 | ✓ |  | ✓ |  | 59.1 | 54.4 | 63.6 | 68.8
3 | ✓ | ✓ |  |  | 62.1 | 56.8 | 65.2 | 69.8
4 | ✓ | ✓ | ✓ |  | 63.7 | 57.8 | 67.0 | 72.6
5 | ✓ | ✓ | ✓ | ✓ | 64.4 | 58.7 | 69.5 | 74.4

###  4.1 Experiment Setting

Dataset. We evaluate our method on two common benchmarks in VI-ReID: SYSU-MM01
Wu et al. (2017) and RegDB Nguyen et al. (2017). SYSU-MM01 is a large-scale
public benchmark for the VI-ReID task, which contains 491 identities captured
by four RGB cameras and two IR cameras in both outdoor and indoor
environments. In this dataset, 22,258 RGB images and 11,909 IR images with 395
identities are collected for training. In the inference stage, the query set
consists of 3,803 IR images with 96 identities and the galley set contains 301
randomly selected RGB images. RegDB is collected by an RGB camera and an IR
camera, which contains 4,120 RGB images and 4,120 IR images with 412
identities. To be specific, the dataset is randomly divided into two non-
overlapping sets: one set is used for training and the other is for testing.

Evaluation Protocols. The experiment follows the standard evaluation settings
in VI-ReID, i.e., Cumulative Matching Characteristics (CMC) Ye et al. (2018)
and mean Average Precision (mAP). Specifically, we present the results of all-
search and indoor-search modes for SYSU-MM01. The RegDB dataset contains two
evaluation settings: Visible2Thermal and Thermal2Visible, i.e., Visible images
as query images to search gallery sets consisting of infrared images and vice
versa.

Implementation Details. The proposed method is implemented on four NVIDIA 3090
GPUs with PyTorch. We adopt the feature extractor in AGW Ye et al. (2022),
which is initialized with ImageNet-pretrained weights to extract
2048-dimensional features. During the training stage, the input images are
resized to 288$\times$144\. We follow augmentations in Ye et al. (2021) for
data augmentation. In one batch, we randomly sample 16 person IDs, and each
person ID samples 16 instances. We set $M$ to be 16 for computational
convenience. We adopt Adam as an optimizer to train the model with an initial
learning rate of 3.5e-4, decaying to 1/10 of the previous value times every 20
epochs. The number of epochs is 100, in which the first 50 epochs are trained
by contrastive loss with the centroid prototype. For the last 50 epochs, we
train the model by contrastive loss with both the hard and dynamic prototypes.
$E_{CPCL}$ is 50. At the beginning of each epoch, we utilize the DBSCAN Ester
et al. (1996) algorithm to generate pseudo labels. During the inference stage,
we use the momentum backbone $\phi_{m}$ to extract features and take the
features of the global average pooling layer to calculate cosine similarity
for retrieval. The momentum value $\alpha$ and $\beta$ is set to 0.1 and
0.999, respectively. The temperature hyper-parameter $\tau$ is set to 0.05 and
the weighting hyper-parameter $\lambda$ in Eq.(20) is 0.5.

###  4.2 Results and Analysis

To comprehensively evaluate our method, we compare our method with 20
supervised VI-ReID methods, 4 semi-supervised VI-ReID methods, and 9
unsupervised VI-ReID methods. The comparison results on the SYSU-MM01 and
RegDB are reported in Tab. 1.

Comparison with USVI-ReID Methods. As shown in Tab. 1, our method achieves
superior performance compared with state-of-the-art USVI-ReID methods. MMM Shi
et al. (2024) is proposed to establish reliable cross-modality correspondences
and is also the current best-performing method. Our method with MMM can
achieve 65.9% in Rank-1 and 61.8% in mAP, which surpasses that of MMM by a
large margin of 4.3% and 3.9%. Notably, our method even without MMM gains the
best performance with 64.4% in Rank-1 and 58.7% in mAP. Although existing
USVI-ReID methods mentioned in Tab. 1 have made great progress in the USVI-
ReID task, the neglect of diverse individual information hinders their further
improvement. They overlook diverse individual information, which often
constitutes hard samples. Thus, we propose progressive contrastive learning
with hard and dynamic prototypes to mine hard samples, which can guide the
model to learn more robust and discriminative features.

Comparison with SSVI-ReID Methods. There are four SSVI-ReID methods proposed
to alleviate the problem of labeling cost by using a part of data annotations
and they have made some progress. However, the application of these methods in
real scenarios is still limited due to the fact that data annotations are
relatively expensive. Remarkably, our method’s performance without any
annotations surpasses that of all methods relying on partial annotation.

Comparison with SVI-ReID Methods. Surprisingly, our method without annotation
outperforms several SVI-ReID methods, e.g., DDAG Ye et al. (2020), AGW Ye et
al. (2022), NFS Chen et al. (2021), LbA Park et al. (2021). This shows the
immense competitiveness of PCLMP compared to SVI-ReID methods that rely on
complete data labeling. The superior performance of PCLMP mainly benefits from
the hard prototype and dynamic prototype contrastive learning. Additionally,
it’s worth acknowledging that there is still a significant disparity between
PCLMP and the state-of-the-art fully-supervised results.

Figure 3: (a) The effect of hyper-parameter $\lambda$ with different values.
(b) The effect of hyper-parameter $k$ with different values. (c) Comparisons
with ARI values of different methods. Figure 4: The t-SNE visualization of 10
randomly selected identities. Different color indicates different IDs. Circle
means visible features and the pentagram means infrared features.

###  4.3 Ablation Study

In this section, we conduct ablation studies on the SYSU-MM01 dataset in both
all-search and indoor-search modes to show the effectiveness of each component
in our method. The results are shown in Tab. 2.

Baseline Settings. We use PGM Wu and Ye (2023) as our baseline. Although PGM
has achieved a promising performance on the USVI-ReID task, the neglect of
hard samples hinders its further improvement.

Effectiveness of HPCL. The HPCL is proposed to mine hard samples at the
cluster level. As shown in Tab. 2, When adding the HPCL on Baseline, the
performance improves a large margin of 5.8% in Rank-1 and 5.1% in mAP,
respectively. It shows that hard samples can be effectively mined using hard
prototype contrast learning, facilitating the model to learn more
discriminative features.

Effectiveness of DPCL. The DPCL is proposed to mine hard samples at the
instance level. The results show that Rank-1 accuracy can be improved by 2.8%
in Rank-1 and 2.7% in mAP when adding the DPCL on Baseline, which confirms
that contrastive learning with dynamic prototype can further mine hard
samples.

Effectiveness of PCL. PCL is introduced to smoothly shift the model’s
attention from simple samples to hard ones. The results show that Rank-1
accuracy can be improved by about 1% in Rank-1 and mAP compared to adding
simultaneously the HPCL and DPCL on the Baseline. This confirms that
progressive contrastive learning plays a valuable role in assisting HPCL and
DPCL in mining hard samples.

Surprisingly, contrastive learning with both hard and dynamic prototypes
significantly exceeds the baseline by a large margin of 8.1% in Rank-1 and
7.0% in mAP. The HPCL and DPCL can complement each other to mine hard samples,
which effectively guides the network to learn more robust and discriminative
features.

###  4.4 Further Analysis

Hyper-parameter analysis. Hyper-parameter $\lambda$ is a weighting parameter
to trade-off $L_{HPCL}$ and $L_{DPCL}$. Fig. 3 (a) presents the results under
different values of $\lambda$. We can observe that when $\lambda$ is small,
i.e., $L_{DPCL}$ contributes more to the model, the performance degrades.
However, when $\lambda$ is large, i.e., $L_{HPCL}$ contributes heavily to the
model, the model both achieves superior performance. Note that when
$\lambda=1$, i.e., the proposed method is trained without DPCL, the
performance drops significantly. $\lambda$ is finally set to 0.5 and our
method achieves the best performance of 64.4% in Rank-1. Moreover, we also
analyze the effect of the number of hard samples at hard prototype. As shown
in Fig. 3 (b), we vary the $k$ from 1 to 3 and keep the other hyper-parameters
fixed, which shows that PCLMP achieves the best performance when $k=1$.

The ARI metric. Following MMM Shi et al. (2024), we utilize the Adjusted Rand
Index (ARI) metric for clustering evaluation. The larger the ARI value, the
higher the clustering quality. In Fig. 3 (c), “RGB” and “IR” denote the ARI
values of visible and infrared clusterings, which can measure the quality of
visible and infrared pseudo-labels. “ALL” means the ARI values of overall
clusterings, which can evaluate the reliability of cross-modality
correspondences. PCLMP surpasses other methods significantly on all of the
mentioned ARI values, which demonstrates our method can effectively mine hard
samples to improve clustering quality.

Visualization. As shown in Fig. 4, we visualize the t-SNE map of 10 randomly
chosen identities from SYSU-MM01. Compared to the baseline, the distribution
of the same identity from the same modality is more compact and the distance
of the same identity from different modalities is closer together. Moreover,
some hard samples in the baseline are incorrectly clustered, while these hard
samples are well clustered in our method, which shows the effectiveness of the
proposed PCLMP.

##  5 Conclusions

In this paper, we propose a novel method for USVI-ReID called Progressive
Contrastive Learning with Multi-Prototype (PCLMP), which learns both shared
and diverse information. To be specific, we design Hard Prototype Contrastive
Learning to mine distinctive yet significant information and Dynamic Prototype
Contrastive Learning to preserve intrinsic diversity features. Furthermore, we
introduce a progressive learning strategy to incorporate both HPCL and DPCL
into the model. Extensive experiments demonstrate that PCLMP outperforms
state-of-the-art USVI-ReID methods.

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