Learning Source-Free Domain Adaptation for Visible-Infrared Person Re-Identification
Authors: Yongxiang Li
Yanglin Feng
Yuan Sun
Dezhong Peng
Xi Peng
Peng Hu
Venue: NeurIPS 2025 poster ()
Keywords: Source-Free Domain Adaptation;  Visible-Infrared Person Re-Identification; Residual Transfer Learning
Primary area: applications

Abstract: In this paper, we investigate source-free domain adaptation (SFDA) for visible-infrared person re-identification (VI-ReID), aiming to adapt a pre-trained source model to an unlabeled target domain without access to source data. To address this challenging setting, we propose a novel learning paradigm, termed Source-Free Visible-Infrared Person Re-Identification (SVIP), which fully exploits the prior knowledge embedded in the source model to guide target domain adaptation. The proposed framework comprises three key components specifically designed for the source-free scenario: 1) a Source-Guided Contrastive Learning (SGCL) module, which leverages the discriminative feature space of the frozen source model as a reference to perform contrastive learning on the unlabeled target data, thereby preserving discrimination without requiring source samples; 2) a Residual Transfer Learning (RTL) module, which learns residual mappings to adapt the target model’s representations while maintaining the knowledge from the source model; and 3) a Structural Consistency-Guided Cross-modal Alignment (SCCA) module, which enforces reciprocal structural constraints between visible and infrared modalities to identify reliable cross-modal pairs and achieve robust modality alignment without source supervision. Extensive experiments on benchmark datasets demonstrate that SVIP substantially enhances target domain performance and outperforms existing unsupervised VI-ReID methods under source-free settings.

Learning Source-free Domain Adaptation for Visible-Infrared Person Re-Identification
Yongxiang Li', Yanglin Feng', Yuan Sun', Dezhong Peng! Xi Peng', Peng Hu'
Colleee of Computer Science, Sichuan University, Chengdu, China.
National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, Chengdu, China. TTanfu Jincheng Laboratory, Chengdu, China. (rhythmli., scu, penghu ml
(rggchu.mmmgggeee J@gmail.com
Abstract
In this paper, we investigate source-free domain adaptation (SFDA) for visible- infrared person re-identification (VI-ReID), aiming to adapt pre-trained source model to an unlabeled target domain without access source data. To address this challenging setting, propose novel learning paradigm, termed Source-Free Visible-Infrared Person Re-ldentification (SVIP), which fully exploits the prior knowledge embedded in the source model to guide target domain adaptation. The proposed framework comprises three key components specifically designed for the source-free scenario: 1) Cource-Guided Contrastive Learning (SGCL) module, which leverages the discriminative feature space of the frozen source model as reference perform contrastive learning the unlabeled target data, thereby preserving discrimination without requiring source samples; 2) a Residual Transfer Learning (RTL) module, which learns residual mappings to adapt the target model's representations while maintaining the knowledge from the source model; and 3) a Structural Consistency- Guided Pross-modal Alignment (SCCA) module, which enforces reciprocal structural constraints between visible and infrared modalities to identify reliable cross-modal pairs and achieve robust modality alignment without source supervision. Extensive experiments on benchmark datasets demonstrate that SVIP substantially enhances target domain performance and outperforms existing unsupervised VI-ReID methods under source-free settings. Code is available at http://ghhubboom///XXX/Xyyy////
Introduction
Visible-Infrared Person Re-Identification (VI-RelD) aims match pedestrian images captured under visible and infrared modalities, achieving consistent cross-modality correspondence across varying lighting conditions, which is particularly valuable in various applications such as nighttime security 6 7). By leveraging complementary visual cues from multiple sensors, VI-ReID systems can maintain consistent performance both daytime and nighttime environments. However, the majority of existing VI-ReID methods rely heavily on scage-scale, well annotated datasets with aligned visible and infrared images. Constructing such datasets demands extensive human annotation and careful sensor calibration, which is labor-intensive, time-consuming, and increasingly constrained by privacy regulations. These limitations severely hinder the scalability and real-world deployment of VI-RelID systems.
and smart surveillance 2 3 4
5
To reduce dependence on labeled data in the target domain, Unsupervised Domain Adaptation (UDA) has emerged as a promising alternative. UDA methods aim to transfer knowledge from a labeled
*Corresponiigg author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Visible Security Cameras
Unsuparvivd
Transfor from Medel
Performase
(a) Fog
R. Performuncs R.1 Performasee
(b) Frest
84
22
Bottleneck
31.17
13:44
Bottleneck
30.1S
Sunny Fog
Frost
Rain
Snow
30
Target Domain
24,87
SDCL
MMM
UAN
SVIP
SDCL
MMM
CAN
SVIP
Inftared Security Cameras
Porforman mane
14
22
(c) Rain
$2.59
(d) Snow
27.4
30-
29.66
Bottleneck
25.58
26
25.85
1 15
Sunny
Fog
Frosi
Rain
Seow
SDCL MMM UAN SVIP
SDCL MMM UAN
SVIP
Figure 1: Our Ohservations. Despite recent advances, state-of-the-ar unsupervised methods such as SDCL 11 and MMM [12] often suffer from performance limitations due to insufficient supervision in the target domain. Incorporating source- domain knowledge through labeled data (for example, UAN [3] or pre-trained models (such as our proposed SVIP) can significantly enhance larget-domain representation and adaptation. For example. we conduct experiments using SYSU-MM01 [14] as the source domain and LLCM-W W under (a) Fog, (b) Frust, (c) Rain, (d) Snow conditions as target domains (see Section to validate this observation.
source domain to an unlabeled target domain [0] achieving notable improvements over purely unsupervised methods, as illustrated in Figure Despite their effectiveness, conventional UDA approaches typically assume joint access to source and target data during training. This assumption is often unrealistic in practical VI-ReID applications, where source data may contain sensitive personal information that cannot be shared or retained due to legal and ethical constraints. Moreover, the need to repeatedly load and process large-scale source datasets introduces significant computational overhead, especially in edge computing scenarios or cesuurce-constraineed environments. These issues collectively restrict the deployment of conventional UDA pipelines in real-world systems.
To address these practical constraints, Source-Free Domain Adaptation (SFDA) has recently garnered attention [15 16 17 18]. SFDA eliminates the need for source data during adaptation by leveraging a pre-trained source model as the sole knowledge carrier. This setting naturally supports privacy- preserving and lightweight model adaptation, aligning more closely with real-world requirements. However, existing SFDA methods are predominantly designed for unimodal tasks and do not general- ize well to VI-ReID, where domain shifts and modality gaps must be addressed simultaneously. In the absence of paired visible-ine inffared samples or annotated target identities, gross-modal association becomes highly uncertain, often leading to noisy feature learning and suboptimal adaptation. These challenges necessitate a new SFDA framework tailored to the unique characteristics of VI-ReID, capable of learning modality-invarial and identity-discee -iieiiit-dieriiinativee representations without relying on source data or cross-modal supervision.
Motivated by these observations, we propose a novel learning paradigm, termed Source-Free Domain Adaptation for Visible-Infrared Person Re-Identification (SVIP). Specifically, our contributions include: 1) the Source Guided Contrastive Learning (SGCL) mechanism, which improves supervision reliability by integrating dual clustering results from both source and target models on target domain samples. It adaptively fuses supervisory signals weighted by confidence scores to mitigate category inconsistency and label noise across modalities, enhancing the discriminative power of the target model; 2) the Residual Transfer Learning (RTL) mechanism, which introduces feature residual distillation loss to explicitly align intermediate feature representations of source and target models under identical inputs, preserving the core discriminative structure from the source domain, thus improving structural stability and generalization: 3) the Structural Consistency Guided Cross-modas Alignment (SCCA) mechanism, which exploits the source model's structural semantic knowledge derived from paired visible-infrared images to guide the target model in mining potential cross- modal similar samples within the unpaired target domain, thereby achieving stable and reliable cooss-modal feature alignment and further enhancing consistency and robustness of cross-modal representations. Collectively, the source model to overcome key challenges in the unsupervised target domain, including knowledge transfer difficulty, and insufficient cross-modal alignment, significantly boosting the adaptability and performance of the target model in cross-modal person re-identification.
effectively transfers discriminative and structural knowledge from
The main contributions of this work could be summarized as follows:

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We propose novel SFDA framework, SVIP, for VI-ReID that adapts pre-trained source model to an unlabeled target domain without requiring access any source data. To the best of our knowledge, this is among the first works exploring SFDA specifically for VI-RelD..
SVIP incorporates three key mechanisms: Source Guided Contrastive Learning (SGCL) to enhance supervision reliability via confidence- weeddeeeewwiggted dual clustering; Residual Transfer Learning (RTL) to align intermediate features and preserve source discriminative structures; and Structural Consistency Guided Cross-modal Alignment (SCCA) to exploit source structural knowledge for robust cross-modal feature alignment in the unpaired target domain.
Extensive experiments under diverse domain adaptation settings demonstrate that SVIP consistently outperforms nine state-of of-the-ar baselines, validating its effectiveness and superiority.
2
Related Works
2.1 Source-free Domain Adaptation
Source-free domain adaptation (SFDA) has emerged as erucial paradigm that addresses the limi- tations of conventional unsupervised domain adaptation (UDA) by eliminating the requirement to
access source domain data during the adaptation process [99 16.
15
17 13]. This characteristic
makes SFDA particularly well-suited to practical scenarios that impose strict privacy constraints, involve decentralized data storage, or demand efficient model deployment in distributed computing environments. Existing SFDA methods can generally be divided into two broad categories. 1) Gencration-basee methods aim to learn Nomainn-nvariane representations by synthesizing or trans- forming target domain samples to approximate the distribution of source domain data. This enables the application of conventional UDA frameworks to reduce domain shifts 20 18 17
22]. 2) Self-training methods adopt iterative learning strategy in which pseudo-labels generated from model predictions on unlabeled target data serve as supervision signals to progressively refine the model in fully unsupervised manner [23 24 25 26 27 While these approaches have demonstrated effectiveness in unimodal settings, their extension to scenarios involving cross-modal or multi-modal data remains limited. Moreover, they often fall short in fully capturing the rich semantic correlations and intrinsic structure present in heterogeneous target domains, which is a significant challenge in complex tasks such re-identification.
2.2 Domain Adaptation for Person RelD
Person Re-Identification (ReID) faces significant challenges due to large domain shifts caused by variations in environment, camera styles, and modalities [28] 29 30). Unsupervised Domain Adaptation (UDA) has attracted much attention for its ability to adapt models to unlabeled target domains without manual annotation [31 32 33 However, UDA methods typically require access to labeled source data during adaptation, which incurs high computational costs and raises privacy concerns, especially in Rell scenarios subject to strict data regulations. These limitations hinder the practical deployment of UDA models. SFDA addresses these issues by adapting pretrained source models to target domains without accessing source data [34 35 36). SFDA aligns well with privacy preservation and deployment constraints. Nonetheless, existing SFDA methods for ReID are primarily designed for unimodal settings and often fail to exploit the intrinsic structure and semantic relationships of the target domain [36] 34], limiting their adaptation effectiveness. In addition, the absence of identity overlap between source and target domains further complicates feature transfer and undermines pseudo-label reliability. These limitations pose significant challenges in mitigating label noise, narrowing modality discrepancies, and achieving effective cross-modal alignment, all of which are crucial for advancing SFDA in RelD.
3 Methodology
3.1 Problem Statement and Notations
For clarity, we define the key notations and terminology used in this paper. Let &g denote the source model trained on a labeled visible-infrared person re-identification (VI-ReID) dataset Ds. Additionally, let the unlabeled and unpaired VI-ReID dataset in the target domain be denoted as
3

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Source Model
Features
SGCL
SCCA
Menury
Carte Morayy
Wriced Mocss
Visible
seeling
OOOO
AAAA
O0OO
AAAA
Infrared
Einagss
Estrogy
Wesgan
Visible
RTL
Target Domain
Chuserting
0
Infrared
4
4
Omg
Target Domain
Target Model
Features
Teacher Model
O/A
A Visible Instunce
A Visible Infrared
Trainable
+
Push Pull
Figure 2: Overview the proposed SVIP framework. SVIP integrates core mechanisms: 1) Source Guided Contrastive Learning (SGCL) improves supervision reliability by combining dual clustering from source and target models with confidence-weighter supervision; 2) Residual Transfer Learning (RTL) aligns intermediate features between source and target models via residual distillation preserve discriminative structures and enhance generalization; 3) Structural Consistency Guided Cross-moda Alignment (SCCA) leverages source domain structural knowledge to guide stable cross-modal feature alignment in the unpaired target domain. Together, these components enable effective source-free domain adaptation for VI-RelI).
Dr Dr= (1= (1] where NV and N are the numbers of visible images x. and infrared images 27 respectively. For convenience, we denote Dr more generally as Dr {>. J N i== where P e [V. Due to the domain shift between the source and target domains, the performance of &s can degrade significantly in practical scenarios, such as those involving environmental changes (c.g., variations in weather conditions). Our objective to adapt 2g to the target domain Dy and obtain target-specifie model Φ without accessing any source domain samples ground-truth labels in the target domain. An overview of the SFDA framework for VI-ReID illustrated in Figure [ The main motivation of our method lies in the observation that the source model 4 contains rich discriminative knowledge and cross-modal alignment capabilities. Selectively transferring this knowledge the target domain can enhance the target model Ø in terms of both discrimination and cross-modal semantic understanding, thereby overcoming the limitations of existing unsupervised methods.
3.2 Source Guided Contrastive Learning
Existing unsupervised methods typically rely on clustering to generate pseudo-labels, which provide supervd-ppeeiiioon signals for training samples. However, due to the absence of prior knowledge, the clustering results are often susceptible to modality gaps, leading to a degradation in pseudo- label quality and resulting in label noise issues 7 37 38
40 41). To alleviate this problem, we propose a Source Guided Contrastive Learning (SGCL) mechanism that leverages the prior knowledge embedded in the source model &3 guide the target model Φr towards more reliable supervision signals.
39
Specifically, SGCL adaptively integrates the dual predictions of target domain samples from both the source model and the target model based on their confidence scores. This produces a more trustworthy supervisory signal, enhancing the understanding and adaptability of the target model by to the target domain Dr. First, we extract features of target domain samples using the source model & and the target model dr. and apply DBSCAN clustering obtain two sets of cluster centers, denoted as C's and Cr:
Cs DBSCAAN(s(x?))) Cr DBBCAN (OTSCA((D(<e?)))
To enhance the stability of clustering representations, we introduce two memory banks: Ms = (mī) Ks Ks and Mr {mī) Kr k== which store the cluster center features obtained by the source and target models, respectively, in the current training cycle. Here, Kş and Kr denote the number

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of clusters for the source and target models, respectively. The memory banks are updated in each training cycle using the exponential moving average (EMA) strategy:
S m& - n + (1-1)
Σ EEC
L (2(
(2)
m t nm + (1 1)
Σ
VECi
where y [0, 1) is the momentum coefficient, and C and C% represent the sample index sets of the k-th cluster for the source and target models, respectively.
Given sample æ!", we compute its similarity to all cluster centers from both models and obtain probability distribution over clusters using the Softmax function:
e mL)/T)
ps(k)
(4)
Kg プド exp((0s (x)
exp( ( (2 (2.
pr (k)
Σ KT expl (Dr (x m
(5)
where is temperature parameter that controls the smoothness of the distribution. Then, the prediction confidence estimated based on the entropy of the distributions, and a fusion weight computed accordingly:
exp(-H(ps))
(6)
exp(-H(ps)) + exp(-H(pr)))
where ps = (1), ... (k), ... (s((ss] and pr = (pr(1), .. (P)))) .M (r H() denotes the Shannon entropy.
Considering the possible inconsistency in the number of clusters between the source and target models (i.e., Kg * K7), directly fusing the full Softmax distributions would lead to dimensional mismatch issues. Therefore, instead of fusing the distributions themselves, we fuse the features based on the respective assigned cluster centers of the sample in each model. This design avoids dimensional inconsistency and enhances fusion accuracy. In other words, for a given sample a. the optimization objective is defined as:
Lass
1
Σ
Σ 7. I(Ds(x)) ). m + (1-7) I(Dr(x?)) m.
No PE(VI)
where m and m.. denote the assigned cluster centers for the sample in the source and target domains,
respectively. I(.,)) represents the InfoNCE loss [42], and N, is the batch size.
This fusion strategy fully exploits the multi-view clustering structures of the same sample from both the source and target models. By adaptively balaneing their supervision signals based on prediction confidence, it enables the target model to benefit from the prior knowledge embedded in the source model. This, in turn, mitigates the clustering noise caused by modality discrepancies and enhances the generalization and adaptability of the target model in the target domain.
3.3 Residual Transfer Learning
Although the source-guided contrastive mechanism introduced the previous section provides stable supervision for the target model to adapt to the target domain, it remains limited in maintaining the consistency of discriminative behavior. To address this issue, we propose Residual Transfer Learning (RTL) mechanism to explicitly align source and target domains. Inspired by the principle of preserving core discriminative knowledge in domain adaptation, this mechanism formalizes the discrepancy in discriminative behavior between the source model and the target model under the same input as a form of feature distillation constraint. This constraint encourages the target model to continuously align its intermediate feature representations with those of the source model during the adaptation process, thereby preserving structural knowledge and maintaining consistency in prediction behavior.
5

---

Specifically, given target-domain sample x,, the residual distillation loss is defined as:
res
( 4r (x?))
(8)
Lres guides the target model to retain the structural knowledge of the source model in the discriminative feature space. Compared to probability distribution alignment approaches, this method does not rely on consistency in the output space the two models, making it more suitable for the scenarios with label space shifts or mismatched output dimensions. Therefore, it offers greater flexibility and stability. Beyond the SGCL, the RTL mechanism further strengthens the continuous guidance from the source model to the target model, thus achieving a more effective balance between generalization and adaptation capabilities.
3.4 Structural Consistency Guided Cross-modal Alignment
In an unlabeled and unpaired target domain, the absence of explicit cross roosl correspondences poses significant challenges to robust alignment. To alleviate this issue, propose Structural Consistency Guided Cross-modal Alignment (SCCA) mechanism, which leverages stable and reliable structural semantic knowledge from the source model to assist the target model in discovering poten- tial semantically consistent image pairs, thereby facilitating consistent cross-modke representation learning.
The source model 4s. trained with supervision on paired visible-infrared images, has acquired strong capabilities in modeling cross-modal structural consistency. To transfer this capability to the unpaired target domain, we jointly exploit the feature modeling of both the source model and the current target model to estimate potential tross-mocke similarities. Given the unpaired target-domain samples {a: (a)))" 1a and {a] y the structural similarity between visible image a. and an infrared image 2. is defined as:
1 (2) Siy = (
- (Dr (ss(()))ss(z))) 2s(xx))
(r(3))))
where Dy provides structural prior guidance, and or reflects the adaptation status of the target model. The similarity score Siy integrates source-domain knowledge and target-domain modeling
capacity, and is used construct cross-modal similarity vectors S. [S. S and
S [S, SSNv), which are employed estimate pseudo matching pairs.
To enhance the accuracy and robustness of the pseudo matches, two structural constraints are imposed: 1) Reciprocity Constraint (Cz): Each image pair must be the most similar each other, i.e. Equation (T0). 2) Lower-bound Similarity Constraint (C2): The structural similarity between the image pair must exceed predefined threshold T, i.e., Equation [I]
These constraints are formally defined as:
arg max S ang Siz1 = iE (VV jE(L..,,77 .x
C1
(10)
arg max S (e(1,..,,T) (E .V))
arg max S,d =j
C2 = (Si 2 7))
27)
(11)
An image pair (m:" 2z)) is regarded as reliable pseudo match if both C₁ and C2 are satisfied. Such pairs are marked using a binary indicator Aij = [[<x)' x }= C₁ A C2). Under the guidance of the structural priors from the source model, this strategy significantly mitigates the uncertainty caused by the lack of pairing.
On this basis, to further improve the compactness of cross-modal pseudo matches in the feature space, we introduce a Cross-modal Reciprocal Consistency Loss:
No Aig log
exp( Pr (2(
Lore
Σ
S.L. Q+P,
(12)
No
Σ exp( ( (2oP), &r ( (10)))/)
PE(V.I)
t=1

---

where т denotes a temperature parameter. This loss employs reliable pseudo matches as supervisory signals and utilizes contrastive learning to enhance semantic consistency between cross-modee images, thereby improving the alignment and discriminative power of cross-modal features.
3.5 Overall Training Procedure
Given that source data is unavailable, the proposed method optimizes the target model Φr in each iteration using only the unlabeled data from the target domain. The entire training process is performed without access to any source domain samples, relying solely on the prior knowledge embedded in the source model 2g guide cross-mode semantic transfer. The optimization objective for the target model integrates three key components and is defined as follows:
Call Laie + Ares + AgCores
(13)
where A and 22 are weighting coefficients.
4 Experiments
4.1 Experiment Settings
Datasets: To systematically investigate domain adaptation in VI-ReID, we conduct experiments on
three widely used datasets: SYSU-MM01 [14), RegDB [43), and LLCM 44 These datasets provide a comprehensive foundation for exploring the challenges and opportunities in cross-modal person re-identification across varying domains.
To comprehensively evaluate the adaptation performance across diverse domains, we define two domain adaptation settings: 1) Basic Setting: This setting is designed to evaluate the model's 's ability to handle common domain shifts, including variations in illumination, camera viewpoints, and background complexity. Four adaptation scenarios considered: (i) SYSU-MMO1 > RegDB, (ii) SMMU- + LLCM, (iii) LLCM - RegDB, and (iv) LLCM SYSU-MM01. SYSU-MM01 and LLCM are used as source domains, while RegDB, LLCM, and SYSU-MM01 serve as target domains accordingly. Due to its relatively small scale, RegDB is not used source domain. 2) Weather Setting: To further evaluate model robustness under realistic environmental variations, we introduce five weather conditions to both RegDB and I.LCM, including Sumny, Fog, Frost, Rain, and Snow. Each condition is simulated three severity levels, resulting in corrupted versions of the datasets, denoted as RegDB-W and LLCM-W. In this setting, the model trained on SYSU-MMO1 is adopted as the source model, and domain adaptation performed on each corrupted target domain. The experiment follows the standard evaluations in VI-ReID [45 46). including Cumulative Matching Characteristic (CMC), and mean Average Precision (mAP).
Domain Adaptation Settings:
During training. pedestrian images are resized to 288 x 144 pixels. The AGW (47) network is employed the feature extractor. The parameters of the source model remain fixed throughout training. The target model is optimized using the Adam optimizer with a weight decay of 8x 10-4, The initial learning rate is set 5x100 10 and is reduced by factor of 10 every 20 epochs. The batch size N₂ is 128, and training proceeds for 50 epochs. The momentum parameter n is fixed at 0. while the temperature hyperparameter 7 is set to 0.05. The clustering algorithm DBSCAN is applied with an epsilon value of 0.6 and a minimum sample size of 4. The threshold T maintained at 0.5. The tra parameters A and X2 are further analyzed in the Supplementary Material. All experiments are performed on an Ubuntu 20.04 system equipped with four NVIDIA RTX 3090 GPUs
Implementation Details:
Training of the Source Model: For the source model, our method could use the model trained by any VI-RelD method. Without loss of generality, follow (3 and present simple yet representative approach with the triplet loss function [45]]] 48 to train a source model for our experiments:
Liri Σ Σ max (10)(0))) &s (>()))1 (x)()))) +s5 Teni, .). (14)
PE(VI
where a.f E Ds represents anchor source training set. a. E Dg and a. E Ds are positive (same identity) and negative (different identity) samples of æ; Teri is the margin, which is set to 0.3.
7

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Table I: Comparisons with the state-of-the-art methods under Basic Setting. R-1 (%). mAP (%) and mINP (%)
are reported. The best results are marked in bold, and the second- best results are underlined.
SYSU-MM0I
ILCCM
SYSU-MMOI
-
RegDB
LLCM - SYSU-MMOI
ILCCM
RegDR
Mothods
V2I
12V
V2T
12V
All
Search
Indoor
Search
12V
T2V
R-1
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
mAP
Source Only
12:47
12.91
12:21
13.13
31.26
28.95
20.35
20.91
24.51
25.48
OTAL Pp
17.88
20.46
14.97
18.66
32.90
29.70
32.10
28.60
29.90
27.10
29.80
38.80
32.90
29.70
32.10
28.60
DVI-ReID
CCLNel 50
30.33
27.12
30.11
26.67
69.94
65.53
70.17
66.66
54,03
50.19
56.68
65.12
69.94
65.53
70.17
66.66
PGM [1]
30.95
33.68
37.82
43.52
69.48
65.41
69.85
65.17
57.27
51.78
56.23
62.74
69.48
65.41
69.85
65.17
GUR 52
31.47
34.77
29.
68
33.38
73.91
70.23
75.00
69.94
60.95
56
64.22
69.49
73.91
70.23
75.00
69.94
SDCL [1]
38.86
45.19
46.34
43.35
86.91
78.92
85.76
77.25
64.49
63.24
71.37
76.90
86.91
78.92
85.76
77.25
MMM 12
35.39
42.57
46.26
89.70
80.50
85.80
77.00
65.90
70.30
74.90
89.70
80.50
85.80
77.00
CMT [53
31.22
33.53
37.37
40.77
72.06
65.01
72.12
62.98
42.15
38.20
45.33
41.87
50.62
44.35
53.21
47.89
SPDA
LEAD
13
29.37
32.73
36.
38.02
75.99
66.38
70.91
62.90
40.88
36.54
43.17
40. 12
48.95
42.76
51,03
45.67
DRU [54
28.48
33 80
36.55
38, 85
76.26
68, 54
71.68
64.43
41.72
37.89
44.65
41.03
49.81
43.52
52.
46.35
SVIP (Ours)
46.20
47.85
47.30
90.10
63.50
71.60
90.20
Table 2: Comparisons with state-of-the-art methods under the Weather Setting (Severity Level 3), reporting R-1 (%) accuracy. The best results are marked in bold, and the second-hest results are underlined.
L.CM--W (Visible Infrared)
RegDR-W (Visible Thermal)
Methods
Sunny
Fog
Frosi
Rai
Snow
Arg.
Sunny
Fog
Frost
Ruin
Snow
Arg.
Source Only
8.07
8.20
8.19
8.70
8.60
16.06
20.80 86
16.33
20.18
11.55
16.06
OTAL
Fv
12.17
13.56
10.01
9.72
12.90
11.67
28.14
30.96
24.44
30.84
17.98
26.47
WVI-ReID
CCLNei
50
23.05
25.86
21.88
27.04
19.82
23.53
42.27
46.84
36.95
45.15
27.26
39.69
PGM
25.16
26.29
25.30
22.52
18.00
23.45
49.77
54.63
43.39
53.55
32.09
46.69
GUR
52
26.39
29.61
22.89
21.76
17.68
23.67
42.12
57.87
42.41
54.09
33.13
45.93
SDCL
11
26.93
31.17
27.11
27.58
23.07
27.17
50.87
61.08
49.21
64.26
39.01
52.89
MMM 12
25.63
32.32
25.88
22.76
49.17
64.28
47.57
64.04
52.95
CMT
53
23.24
29.74
20.49
21.16
15.97
22.12
48.03
60.36
43.63
62.01
32.78
49.36
SFDA
LEAD
15
22.34
30.16
19.79
19:75
17.51
21.91
45.89
63.23
42.06
60.81
33.73
49.15
DRU 54
20.57
31 48
23.33
22.
18. 16
23 27
46.46
64.74
43.30
61.34
39.82
51.13
SVIP((Ous)
28.02
36.13
32.19
27.48
30.87
54.32
65.92
49.54
67.46
46.05
57.81
4.2 Comparison with the State-of-the-Arts
We compare our SVIP method with nine state-of-the-arr approaches, categorized into two groups. The first group comprises six Unsupervised Visible-Infrared Person Re-Identification (UVI-ReID) methods: OTAL 4 CCLNet (50), PGM [51), GUR (52), SDCL (II), and MMM (12), which are trained exclusively on the target domain. The second group consists of three Source- Free Domain Adaptation (SFDA) methods: CMT [53) LEAD [15), and DRU [54). which involve training a source model and performing domain adaptation following the authors' recommended procedures. Based on the results shown in Tables [ and several important observations can be made:
SVIP consistently achieves superior performance across all benchmarks under both the Basic Setting and Weather Setting, highlighting its strong generalization capability. In some scenarios, the R-1 accuracy exceeds that of the second-best method by over 7%, reflecting its robustness in handling challenging conditions.
Effective exploitation of source domain knowledge plays a crucial role in enhancing the understanding of the target domain, which largely contributes to SVIP's superiority over existing UVI-ReID methods that rely solely on target domain data.
SFDA baseline methods are generally constrained by the assumption of shared classes between the source and target domains, which hinders their ability to address the challenge of non-overlapping classes (identities) in VI-ReID. This limitation often results in suboptimal performance, and in certain cases, even inferior outcomes compared to UVI-RelID.
4.3 Ablation Study
We conduct comprehensive ablation studies to assess the individual contributions of the key compo- nents in SVIP, namely SGCL,, RTI, and SCCA, to the overall adaptation performance. Specifically, progressively remove each component and evaluate the resulting performance degradation on
8

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the target domain. As reported in Table 3 the exclusion of any single component leads to notable drop in re-identification accuracy, which clearly demonstrates the importance of each mechanism in enhancing domain adaptation.
Table 3: Ablation studies on Basic Setting, where RegDB dataset as target domain.
SYSU-MMOI
RegDB
ReyDB LLCM
SGCL RTL SCCA
T2V
V2T
T2V
mAP
85.26
75.64
82.13
72.45
84.32
76.68
81.05
71.32
83.71
73.82
80.59
70,28
82.47
74.13
79.24
69.85
81.95
71.36
78.43
68.17
80.63
71.89
77.52
67.11
87.39
77.25
74.83
86.21
78,04
73.62
87.17
78.06
85.04
75.91
87.35
78.97
84.26
76.13
86.82
76.43
83.27
73.85
85.74
77.32
82.39
72.68
V
90.13
81.26
86.92
77.89
90.20
81.64
86.54
78.51
4.4 Visualization on the Domain Adaptation
To systematically evaluate our method's capability in mitigating domain shift, we conduct t-SNE visualization under the Basic Setting with RegDB target domain. As shown in Figure we analyze feature distributions across four configurations using five identities (20 images each) from source and target domains. Based on the experimental results, we make the following observations: I) Source Only exhibits severe domain gap with separated source (red) and target (blue) clusters; 2) SGCL partially reduces domain discrepancy through confidence-weightedd weighted dual clustering but retains scattered distributions due to insufficient cross modal aligmment: 3) SGCL+RTL enhances structural stability by aligning feature residuals, though noise persists from imperfect sample filtering; 4) The full framework (SGCL+RTT) CGGCLARTL+SCCA achieves tight cross-domare clustering through SCCA-guided structural consistency constraints and confidence-aware filtering. This is clearly shown in the t-SNE visualization, where corresponding blue circles and triangles are closely clustered, reflecting accurate alignment between matched samples. The progressive improvements across different stages further validate the effectiveness of our method in establishing stable cross-modal correspondences and effectively mitigating domain shift.
(a) Source Only.
(b) SGCL.
(c) SGCL+RTL.
(d) SGCL+RTL+SCCA
Figure 3: The Red and Blue denote source and target domains, respectively, while and denote visible and infrared images (best viewed in color).
5 Conclusion
This paper introduces a novel framework for source-free domain adaptation in visible-infrared person re-identification (VI-ReID), termed SVIP. Without access to source data, SVIP effectively leverages the pretrained source model through three tailored components: 1) a Source Guided Contrastive Learning (SGCL) mechanism that improves supervision quality via dual-model clustering and confidence- -eeideeeeeweggheed supervision; 2) a Residual Transfer Learning (RTL) mechanism that distills transferable knowledge through intermediate feature alignment; and a Structural Consistency Guided Cross-modal Alignment (SCCA) mechanism that exploits semantic structure from the source model to guide cross-modal association the unpaired target domain. These designs collectively enhance target model adaptation under the SFDA setting. Future extensions include generalizing SVIP to other multi-modal retrieval tasks (e.g., video-text RelD), and exploring continual adaptation in streaming settings with evolving target domains.

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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 62472295, 62176171, U24B20174, and 62372315; in part by the Fundamental Research Funds for the Central Universities under Grants CJ202303 and CJ202403; in part by the Sichuan Science and Technology Planning Project under Grants 24NSFTD0130, 2024ZDZX0004, and 2024NSFTD0049; part by the Chengdu Science and Technology Project under Grant 002-0XX00000004--XX and in part by the TCL Science and Technology Innovation Fund.
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Reviews:
[{"review_id": "YiyOuZ2evg", "reviewer": "Reviewer_HCDg", "summary": "The paper proposes SVIP, a source-free domain adaptation framework for visible-infrared person re-identification that exploits the prior knowledge embedded in the source model  to guide target domain adaptation. SVIP consists of three mechanisms: (1) Source Guided Contrastive Learning (SGCL), which uses the discriminative feature space of the fixed source model to perform contrastive learning on unlabeled target data; (2) Residual Transfer Learning (RTL), which learns residual mappings to adapt the target model's representations while preserving the source model's knowledge; and (3) Structural Consistency Guided Cross-modal Alignment (SCCA), which enforces structural consistency between visible and infrared modalities to identify reliable cross-modal pairs for alignment. Experiments on benchmark datasets demonstrate that SVIP significantly improves target domain performance and outperforms prior unsupervised VI-ReID methods under source-free settings.", "questions": "1.Could the authors provide the performance of the source model directly evaluated on the target domain without adaptation? Additionally, how much improvement does the proposed method achieve compared to this baseline? This comparison would help clarify the effectiveness of the adaptation process.\n2.It would be helpful if the authors could include qualitative visualizations that illustrate typical failure cases of existing methods and explain why the proposed method succeeds in these scenarios. Could such visual comparisons be added?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "This paper is well-motivated for source-free domain adaptation in visible-infrared person re-identification, introducing novel mechanisms (SGCL, RTL, SCCA) that effectively leverage the frozen source model to guide target adaptation without requiring source data. However, the paper lacks discussion on the capability of the source model, and also misses some qualitative visualizations explaining why some cases fail with other methods but succeed with the proposed method.", "was_revised": true, "final_justification": "I appreciate the author's detailed response, which has addressed my concerns. Accordingly, I will revise my rating.", "rebuttal": "**Q1: Could the authors provide the performance of the source model directly evaluated on the target domain without adaptation? Additionally, how much improvement does the proposed method achieve compared to this baseline? This comparison would help clarify the effectiveness of the adaptation process.**\n\n**A1:** Thank you for your insightful question. To evaluate the effectiveness of the proposed domain adaptation process, we report the performance of the source model directly evaluated on the target domain without any adaptation, denoted as Source Only in Tables 1 and 2. This baseline represents the scenario where a model trained on the source domain is applied to the target domain without any further learning or adjustment. As expected, due to the significant domain and modality gaps in visible-infrared person re-identification, the source model alone performs poorly across all target domains, clearly justifying the necessity of domain adaptation.\n\nIn the basic domain adaptation setting (Table 1), we observe that the Source Only model yields substantially lower Rank-1 accuracies. For example, when adapting from SYSU-MM01 to RegDB (V2T), the source model achieves only 31.26% Rank-1 accuracy, while our method SVIP achieves 90.10%, yielding a remarkable improvement of 58.84%. Similarly, for SYSU-MM01 to LLCM (V2I), the source model obtains 12.47%, while SVIP reaches 40.50%, showing an improvement of 28.03%. In the LLCM to RegDB setting, the source-only performance is 24.51%, whereas SVIP achieves 90.20%, with a significant gain of 65.69%. These consistent and substantial improvements across different adaptation scenarios confirm the effectiveness of our framework in leveraging the pretrained source model to boost target-domain performance under the source-free setting. Furthermore, we evaluate our method in more challenging conditions in the weather-degraded setting (Table 2), where environmental corruptions such as fog, frost, rain, and snow are introduced. In the foggy version of LLCM-W, the source model achieves only 8.20% Rank-1 accuracy, whereas SVIP improves this to 36.13%, yielding a gain of 27.93%. On the snow condition of RegDB-W, the source model achieves 11.55%, and SVIP reaches 46.05%, improving by 34.50%. These results further demonstrate the robustness and adaptability of our approach under adverse conditions.\n\nIn summary, the proposed SVIP consistently and significantly outperforms the source model baseline across various domains and weather settings. This clearly verifies the effectiveness of the adaptation process and highlights the importance of exploiting the source model’s prior knowledge through our carefully designed mechanisms for cross-modal domain adaptation.\n\n---\n\n**Q2: It would be helpful if the authors could include qualitative visualizations that illustrate typical failure cases of existing methods and explain why the proposed method succeeds in these scenarios. Could such visual comparisons be added?**\n\n**A2:** We sincerely appreciate the reviewer’s constructive suggestion. Overall, existing methods tend to produce incorrect matches in scenarios involving large modality discrepancies or complex background clutter, primarily due to inadequate cross-modal alignment and limited structural guidance. In contrast, our method effectively addresses these challenges by incorporating source-guided structural priors through the SCCA module and applying confidence-weighted supervision via SGCL. These mechanisms enable more robust and semantically aligned representation learning. The qualitative comparisons in the supplementary material clearly demonstrate that SVIP achieves more accurate and consistent retrieval results in challenging cases where prior approaches often fail."}, {"review_id": "Zlz9OBEEzM", "reviewer": "Reviewer_iw9b", "summary": "This paper proposes SVIP  for Source-Free Domain Adaptation (SFDA) in Visible-Infrared Person Re-Identification (VI-ReID). Unlike existing methods that require access to source data during adaptation, SVIP relies solely on a pre-trained source model and unlabeled target domain data, addressing both privacy and deployment constraints in practical scenarios. The method introduces three key mechanisms: Source Guided Contrastive Learning (SGCL) for more reliable pseudo-supervision, Residual Transfer Learning (RTL) for structural knowledge distillation, and Structural Consistency Guided Cross-modal Alignment (SCCA) for effective cross-modal pair mining. Experiments on three benchmarks and under multiple weather conditions show that SVIP significantly outperforms state-of-the-art baselines.", "questions": "(1)\tGiven the focus on real-world deployment (e.g., privacy, computation), it would be helpful to understand the computational cost of adapting SVIP to a new target domain. Could the authors report the typical adaptation time or GPU hours for one domain setting, and how it compares with conventional UDA methods requiring source data?\n\n(2)\tIn the SCCA module, pseudo cross-modal pairs are selected based on two constraints, including a similarity threshold T = 0.5 (Eq. 11). How sensitive is the alignment performance to this threshold? Including a brief justification for this value or analysis of its impact would improve transparency.\n\n(3)\tEquations (4)–(5) and (12) involve the use of a temperature parameter τ in Softmax-based similarity computations. The implementation section fixes τ to 0.05, but its influence on model behavior (e.g., distribution smoothness, sensitivity to confidence fusion) is not discussed. Could the authors briefly comment on how τ was selected and whether SVIP is robust to its variation?", "limitations": "The paper clearly acknowledges limitations such as performance dependency on the generalizability of the source model and potential challenges under large domain gaps (Appendix E). These are reasonable and well-contextualized. Further discussion on failure modes (e.g., retrieval errors) could complement this.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Strengths:** This work addresses an important and underexplored problem, i.e. SFDA for VI-ReID, which has strong practical relevance, especially in privacy-sensitive and resource-constrained environments. The formulation and empirical results make a compelling case for the practical deployment of the method. \n\n\n**Weaknesses:** While the contributions are solid, some design choices (e.g., hyperparameter sensitivity and impact of source model quality) could benefit from further clarification. Besides, a more quantitative analysis of failure cases and feature distributions would strengthen the empirical section.", "was_revised": true, "final_justification": "All my concerns have been addressed, and I have therefore raised my rating.", "rebuttal": "**Q1: Given the focus on real-world deployment (e.g., privacy, computation), it would be helpful to understand the computational cost of adapting SVIP to a new target domain. Could the authors report the typical adaptation time or GPU hours for one domain setting, and how it compares with conventional UDA methods requiring source data?**\n\n**A1:** We thank the reviewer for raising this important point. Since SVIP is designed for real-world deployment, we pay close attention to computational efficiency in addition to accuracy. SVIP performs adaptation using only the unlabeled target-domain data and a fixed source model, without accessing any source data. This design naturally reduces computational costs and avoids privacy concerns associated with source data sharing.\n\nTo clarify the adaptation cost, we compare SVIP with a representative conventional UDA method (i.e., UAN) that requires both source and target data, under the SYSU-MM01→RegDB setting. All experiments are conducted on a single NVIDIA RTX 3090 GPU. Specifically:\n\n- SVIP completes adaptation in approximately 2.3 hours, with a peak GPU memory usage of 10.2 GB.\n- In contrast, UAN requires 6.0 hours and 14.1 GB of memory.\n- Moreover, SVIP operates without any access to source data, while UAN must load and process both source and target datasets.\n\nThese results demonstrate that SVIP reduces adaptation time by more than 50% and significantly lowers GPU memory consumption. This efficiency can be attributed to two key design choices:\n\n- SVIP eliminates the need to load or augment source data during training.\n- The source model remains frozen, such that only the target model is updated.\n\nThese properties make SVIP particularly well-suited for privacy-sensitive and resource-constrained deployment environments.\n\n---\n\n**Q2: In the SCCA module, pseudo cross-modal pairs are selected based on two constraints, including a similarity threshold T = 0.5 (Eq. 11). How sensitive is the alignment performance to this threshold? Including a brief justification for this value or analysis of its impact would improve transparency.**\n\n**A2:** We thank the reviewer for the thoughtful question regarding the similarity threshold $T = 0.5$ used in the SCCA module (Eq. 11). This threshold plays an important role in filtering reliable pseudo cross-modal pairs during structural consistency alignment.\n\nWe select $T = 0.5$ based on empirical observations to balance two competing factors: precision and coverage. A lower threshold tends to include more sample pairs but may introduce noisy matches, while a higher threshold improves pair quality but may exclude useful pairs, especially in the early stages of training.\n\nTo assess the sensitivity of our method to this parameter, we conducted an ablation study under the SYSU-MM01→RegDB (V2T) setting by varying $T$ across the range $[0.3, 0.4, 0.5, 0.6, 0.7]$. The results are summarized as follows:\n\n| $T$ | Rank-1 | mAP     | \n|---------------|------------------|---------|\n| 0.3           | 87.41%           | 77.92%  | \n| 0.4           | 89.06%           | 80.34%  | \n| **0.5**       | **90.13%**    | **81.26%** |\n| 0.6           | 89.48%           | 80.71%  |\n| 0.7           | 87.22%           | 78.15%  |\n\nThese results indicate that:\n\n- Overly low thresholds lead to noisy supervision due to inclusion of unreliable pairs.\n- Overly high thresholds result in insufficient training signals due to overly strict filtering.\n\nTherefore, $T = 0.5$ provides a favorable balance between pair quality and quantity, and we use it consistently across all experiments to ensure stable and reliable performance.\n\n---\n\n**Q3: Equations (4)–(5) and (12) involve the use of a temperature parameter τ in Softmax-based similarity computations. The implementation section fixes τ to 0.05, but its influence on model behavior (e.g., distribution smoothness, sensitivity to confidence fusion) is not discussed. Could the authors briefly comment on how τ was selected and whether SVIP is robust to its variation?**\n\n**A3:** We thank the reviewer for the helpful question about the temperature parameter $\\tau$, which controls the sharpness of the Softmax distribution in both confidence estimation (Eqs. 4–5) and contrastive learning (Eq. 12).\n\nWe chose $\\tau = 0.05$ based on empirical observations. To evaluate the sensitivity of our model to this parameter, we vary $\\tau$ in the range $[0.01, 0.03, 0.05, 0.07, 0.10]$ and test performance on the SYSU-MM01→RegDB (V2T) setting. The results are as follows:\n\n| $\\tau$ | Rank-1 | mAP     |\n|--------------|------------------|---------|\n| 0.01         | 88.74%           | 78.62%  |\n| 0.03         | 89.65%           | 80.73%  |\n| **0.05**     | **90.13%**       | **81.26%** |\n| 0.07         | 89.12%           | 79.83%  |\n| 0.10         | 89.12%           | 79.83%  |\n\nThese results indicate that SVIP maintains relatively stable performance across a wide range of $\\tau$ values, demonstrating robustness to this hyperparameter. The best performance is achieved at $\\tau = 0.05$, which offers a good balance between confidence sensitivity and training stability. We therefore fix $\\tau = 0.05$ in all experiments and will include this analysis in the supplementary material for completeness."}, {"review_id": "L00v3BktTD", "reviewer": "Reviewer_C6FV", "summary": "This paper addresses the problem of Source-Free Domain Adaptation (SFDA) in the context of Visible-Infrared Person Re-Identification (VI-ReID), where source domain data is unavailable due to privacy or deployment constraints. To tackle the challenges of modality gap, noisy pseudo-labels, and structural misalignment, the authors propose SVIP, a novel framework composed of three core modules: 1) Source Guided Contrastive Learning (SGCL), which dual clustering from both source and target encoders to provide reliable supervisory information; 2) Residual Transfer Learning (RTL), which aligns intermediate features via a residual distillation loss; 3) Structural Consistency Guided Cross-modal Alignment (SCCA), which mines cross-modal pairs in the target domain by leveraging structural priors from the source model. Extensive experiments on multiple VI-ReID benchmarks, including varying weather conditions, show that SVIP consistently outperforms state-of-the-art SFDA and unsupervised VI-ReID methods. The approach is plug-and-play and readily applicable in real-world scenarios.", "questions": "Please clarify the following points to further strengthen the paper:\n\nI: The implementation uses AGW as the backbone feature extractor. Could the authors clarify whether SVIP is compatible with other VI-ReID architecture? Have any preliminary tests been performed to confirm generalizability across backbones?\n\nII: In SGCL, the confidence weight γ (Eq. 6) balances the influence between source and target cluster predictions. Could the authors provide an intuitive explanation (or a short plot) of how γ evolves over training? This may help understand the dynamics of source vs. target reliance.\n\nIII: The framework relies on DBSCAN clustering in both source and target models to generate pseudo-labels. While the use of memory banks and EMA improves stability, could the authors comment on whether the number of clusters fluctuates significantly across epochs? A brief mention of clustering consistency (e.g., variance in cluster count) would help assess robustness.\n\nIV: In VI-ReID, the identities in the source and target domains typically do not overlap. Could you please clarify whether the proposed SVIP method assumes any partial overlap between the identity classes of the two domains, or is it completely agnostic to the label space?", "limitations": "The authors provide a candid discussion of potential limitations, including the dependence on source model generalizability and challenges posed by large domain gaps (Appendix E). These are appropriate and well contextualized. No critical omissions were found.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "This paper demonstrates high technical quality, with a well-structured methodology and solid empirical validation. Ablation studies and visualizations provide clear evidence of each module’s contribution.\n\nThis work could provide slightly more detail on how general the method is to different backbone architectures, and could elaborate on the failure cases to identify current boundaries of the method's robustness.", "was_revised": true, "final_justification": "I appreciate the author's response, which provided ample clarification and addressed my concerns. Therefore, I'm willing to maintain my rating.", "rebuttal": "**Q1: The implementation uses AGW as the backbone feature extractor. Could the authors clarify whether SVIP is compatible with other VI-ReID architectures? Have any preliminary tests been performed to confirm generalizability across backbones?**\n\n**A1:** We thank the reviewer for the insightful question. Although our implementation adopts the AGW baseline as the default backbone due to its widespread use and strong performance in VI-ReID tasks, we emphasize that the proposed SVIP framework is architecture-agnostic. All core modules in SVIP, including SGCL, RTL, and SCCA, are designed to operate on feature embeddings, not on architecture-specific outputs. These modules make no assumptions about the internal structure of the backbone and can work with any feature extractor that outputs modality-invariant embeddings for visible and infrared images. In preliminary tests, we have applied SVIP with other backbones such as ResNet-50 and OSNet. These experiments show consistent adaptation trends, confirming that SVIP generalizes well across different backbone architectures. Further results will be included in the supplementary material.\n\n---\n\n**Q2: In SGCL, the confidence weight γ (Eq. 6) balances the influence between source and target cluster predictions. Could the authors provide an intuitive explanation (or a short plot) of how γ evolves over training? This may help understand the dynamics of source vs. target reliance.**\n\n**A2:** We appreciate the reviewer’s insightful question. In SGCL, the confidence weight $\\gamma$ defined in Equation (6) is designed to adaptively balance the contributions of the source and target cluster predictions based on their respective confidence levels, which are measured via entropy. This mechanism is especially crucial in the context of unsupervised domain adaptation, where the quality of pseudo-labels from the target domain varies throughout training.\n\nAt the beginning of training, the target model, having not yet adapted to the new domain, generally produces uncertain predictions with high entropy. In contrast, the source model retains relatively stable and confident predictions, reflected in lower entropy. As $\\gamma$ is calculated based on the relative entropy of source and target predictions, this results in higher $\\gamma$ values (typically around 0.7–0.8) in the early stages. Consequently, the loss function gives greater emphasis to the more reliable source-side predictions during this period.\n\nAs training progresses, the target model gradually adapts to the target domain, and its predictions become more confident. This improvement reduces the entropy of the target-side pseudo-labels, thereby decreasing the $\\gamma$ value. Eventually, $\\gamma$ tends to stabilize around 0.5, indicating a more balanced reliance between source and target predictions. This smooth evolution of $\\gamma$ over time effectively captures the dynamic shift from source-guided supervision in the early phase to target-adaptive learning in the later stages. To provide a more intuitive understanding of this process, we will include a visualization of the $\\gamma$ values across training epochs in the appendix. This plot will clearly demonstrate how the model transitions from strong dependence on the source model to increasing confidence in the target model as learning progresses.\n\n---\n\n**Q3: The framework relies on DBSCAN clustering in both source and target models to generate pseudo-labels. While the use of memory banks and EMA improves stability, could the authors comment on whether the number of clusters fluctuates significantly across epochs?**\n\n**A3:** We appreciate the reviewer’s insightful comment regarding clustering stability. In SVIP, DBSCAN clustering is performed independently for both source and target models in each epoch. While some fluctuation in the number of clusters is expected due to evolving feature representations, we observe that such variation is limited in practice, for several reasons:\n\n- Memory Banks and Exponential Moving Average (EMA) (Section 3.2, Eq. 2–3) ensure smooth updates of feature embeddings across epochs, preventing sudden cluster shifts.\n- The SGCL module uses confidence-weighted fusion of clustering results, reducing the impact of minor variations in cluster counts.\n- Instead of using full probability distributions which would require aligned cluster dimensions, our framework applies feature-level contrastive learning using cluster centers (Eq. 7), making it robust to cluster number mismatches.\n\nIn summary, although DBSCAN is non-parametric and adaptive, SVIP is resilient to its dynamic behavior, and clustering consistency is implicitly stabilized by design. We will report the average and variance of cluster counts over training in the supplementary material.\n\n---\n\n**Q4: In VI-ReID, the identities in the source and target domains typically do not overlap. Could you please clarify whether the proposed SVIP method assumes any partial overlap between the identity classes of the two domains, or is it completely agnostic to the label space?** \n\n**A4:** Thank you for raising this important question. We confirm that SVIP does not assume any identity overlap between the source and target domains. The method is fully label-space agnostic, designed specifically for realistic VI-ReID scenarios where identity sets across domains are non-overlapping due to surveillance environment differences.\n\nKey points ensuring label-space independence:\n\n- SGCL aggregates clustering outputs from source and target models using entropy-based fusion, without any identity alignment.\n- RTL promotes feature-level consistency between the two models, enforcing alignment in intermediate representations, not in class labels.\n- SCCA discovers pseudo cross-modal pairs based on structural similarities within the target domain only, guided by the source model’s relational structure, not by matching identities.\n\nThese design choices enable SVIP to work in fully unsupervised and identity-disjoint settings, as demonstrated in our experiments on SYSU-MM01 → RegDB and RegDB → SYSU-MM01, where the source and target identity sets are strictly non-overlapping (see Table 1 and Section 4.3)."}, {"review_id": "dXzvBCWcyv", "reviewer": "Reviewer_xdkT", "summary": "This paper presents SVIP, a novel Source-Free Domain Adaptation (SFDA) framework for Visible-Infrared Person Re-Identification (VI-ReID). The core innovation lies in adapting a pre-trained source model to an unlabeled target domain without access to either source data or target labels, thereby effectively addressing privacy concerns and real-world deployment constraints.", "questions": "1.Equations (2)–(3) introduce an EMA momentum parameter (η = 0.1) to stabilize the cluster centers. While this choice appears reasonable, it would be helpful to briefly discuss the sensitivity of the model to different η values. Does performance degrade significantly when η is increased or decreased?\n\n2.Section 4.1 states that SVIP can incorporate any VI-ReID method as the source model. In Figure 6, stronger baselines (e.g., MMM) seem to yield better adaptation performance. Could the authors confirm whether this trend holds consistently? Such evidence would further underscore the plug-and-play nature and generality of SVIP.\n\n3.Figure 3 qualitatively demonstrates that SCCA produces more compact clusters in the t-SNE visualization. To support this, could the authors provide a quantitative comparison for “SGCL + RTL” versus the complete SVIP pipeline? For instance, the average L2 distance between matched visible-infrared pairs in the target domain.\n\n4.Figures 7 and 8 present qualitative retrieval results. Are there any common patterns among the failure cases (e.g., extreme pose variation, occlusion, background clutter)? A brief analysis could help clarify the limitations and potential directions for future improvement.", "limitations": "As noted in Appendix E, the performance of SVIP partially depends on the generalizability of the source model, which may pose challenges when encountering large domain gaps between source and target domains.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\n1) The three-pronged approach (SGCL/RTL/SCCA) is novel and well-motivated for SFDA in cross-modal ReID. The design elegantly leverages source model priors without data access. 2) The method addresses critical real-world constraints such as privacy protection and computational efficiency, which are often overlooked in existing VI-ReID literature. Moreover, the inclusion of weather robustness experiments (fog, rain, snow) enhances the practical relevance of the work. \n\nWeaknesses: \n\nAlthough the model's robustness under various weather conditions has been demonstrated, its performance under more extreme conditions remains unexplored and warrants further investigation, such as image degradation caused by temperature-induced camera noise.", "was_revised": true, "final_justification": "All my concerns have been resolved by the authors, and in light of the feedback from other reviewers, I am in favor of accepting this work.", "rebuttal": "**Q1: Equations (2)–(3) introduce an EMA momentum parameter ($\\eta=0.1$) to stabilize the cluster centers. While this choice appears reasonable, it would be helpful to briefly discuss the sensitivity of the model to different η values. Does performance degrade significantly when η is increased or decreased?** \n\n**A1:** Thank you for the insightful comment. In our framework, the EMA momentum parameter $\\eta=0.1$ in Equations (2)–(3) is employed to update the memory bank representations of cluster centers in a temporally smooth manner. This helps suppress short-term fluctuations caused by noisy or uncertain pseudo-labels and provides a more stable reference for downstream learning.\n\nTo evaluate the sensitivity of this parameter, we conducted experiments by varying $\\eta$ within a reasonable range. We observed that the model maintains stable performance across different values, indicating robustness to this hyperparameter. Specifically: 1) When $\\eta=0.3$, the updates become overly smooth, causing the cluster centers to lag behind the evolving feature space. This leads to slightly slower convergence and weaker response to recent representation shifts. 2) When $\\eta=0.01$, the cluster centers react too quickly to transient feature variations, which introduces minor instability during early training, especially when pseudo-label quality is still low. However, this does not significantly degrade final performance.  \n\nBased on these observations, we set $\\eta=0.1$ as it provides a balanced trade-off between temporal stability and responsiveness, and we use this value consistently in all experiments.\n\n---\n\n**Q2: Section 4.1 states that SVIP can incorporate any VI-ReID method as the source model. In Figure 6, stronger baselines (e.g., MMM) seem to yield better adaptation performance. Could the authors confirm whether this trend holds consistently? Such evidence would further underscore the plug-and-play nature and generality of SVIP.**\n\n**A2:** We thank the reviewer for highlighting this important point. As stated in Section 4.1, SVIP is designed to be compatible with any VI-ReID method as the source model, reflecting its plug-and-play nature.\n\nTo validate this, we conducted additional experiments using different source models, including both moderate (e.g., OTAL) and stronger (e.g., MMM) VI-ReID baselines. As shown in Figure 6, we observe a consistent trend: better-performing source models generally lead to improved adaptation results under our SVIP framework.\n\nThis is expected, as stronger source models encode richer discriminative and structural knowledge, which our SGCL, RTL, and SCCA modules can more effectively transfer to the target domain. These results confirm the generality and flexibility of SVIP, and we appreciate the reviewer’s suggestion to emphasize this trend.\n\n---\n\n**Q3: Figure 3 qualitatively demonstrates that SCCA produces more compact clusters in the t-SNE visualization. To support this, could the authors provide a quantitative comparison for “SGCL + RTL” versus the complete SVIP pipeline? For instance, the average L2 distance between matched visible-infrared pairs in the target domain.**\n\n**A3:** Thank you for the insightful suggestion. To further support the qualitative t-SNE visualization presented in Figure 3, which highlights the effectiveness of the proposed SCCA module in producing more compact feature clusters, we conducted an additional quantitative analysis. Specifically, we compare the “SGCL + RTL” variant (i.e., without SCCA) against the full SVIP pipeline (with SCCA) by computing the average L2 distance between matched visible-infrared image pairs in the target domain. Feature embeddings are extracted from each model variant, and matched pairs are determined based on pseudo-labels.\n\nUnder the Basic Setting with RegDB, the “SGCL + RTL” variant yields an average L2 distance of 0.812, whereas the full SVIP model reduces this to 0.623. These results provide quantitative evidence that the inclusion of SCCA significantly enhances cross-modal feature alignment, yielding more compact and semantically consistent embeddings. This aligns well with the visual trends observed in Figure 3, thereby validating the effectiveness of SCCA beyond qualitative inspection.\n\n---\n\n**Q4: Figures 7 and 8 present qualitative retrieval results. Are there any common patterns among the failure cases (e.g., extreme pose variation, occlusion, background clutter)? A brief analysis could help clarify the limitations and potential directions for future improvement.**\n\n**A4:** We thank the reviewer for the thoughtful comment. As suggested, we have carefully examined the failure cases illustrated in Figures 7 and 8, which present representative retrieval examples under both the Basic Setting and the Weather-Degraded Setting. In particular, most failure cases are attributed to modality-specific domain gaps. These conditions hinder the model’s ability to extract modality-invariant and identity-discriminative features. Such issues are especially pronounced when infrared samples contain limited structure or are strongly affected by weather conditions.\n\nWhile our method is robust under typical scenarios, these challenging edge cases reveal that subtle cross-modal semantic discrepancies remain difficult. Future directions to address these limitations may include: 1) Foreground modeling to suppress background noise; 2) Modality-aware feature disentanglement; 3) Structure-aware attention mechanisms; 4) Leveraging auxiliary priors such as pose estimation or vision-language alignment. We believe these enhancements could further strengthen robustness in real-world, unconstrained VI-ReID scenarios."}]

Author rebuttal:

Meta-review: Four experts in the field reviewed this paper. Their recommendations are 2 Borderline Accepts and 2 Accepts. This paper presents an innovative source-free domain adaptation (SFDA) method for visible–infrared person ReID. This is relevant in privacy-sensitive and resource-constrained application scenarios. Overall, the reviewers appreciated the paper because the proposed SFDA method integrates the SGCL, RTL, and SCCA components. They are well motivated, and allow leveraging the source model to guide adaptation across visible and infrared modalities. Reviewers also highlighted the strong empirical results of the proposed method, noting substantial improvements over SOTA UDA and SFDA methods on SYSU-MM01, RegDB, LLCM, and synthetic weather perturbations data. The ablation studies on hyperparameters, e.g., similarity threshold and temperature parameter, were also viewed positively.

In the discussions and rebuttal, Reviewers noted the missing discussion on source model capability, the lack of qualitative visualizations and analysis of failure cases, and requested an analysis of computational complexity, and sensitivity to thresholds. These concerns were well addressed in the rebuttal, and two of the reviewers raised their ratings. The paper makes a decent contribution to the adaptation for cross-modal ReID , and the empirical improvements are substantial. The weaknesses are minor and were largely addressed during the rebuttal. Based on the reviewers’ feedback and the authors’ satisfactory rebuttal that addresses most concerns, I recommend it for acceptance. The reviewers raised some important issues and concerns in the Weaknesses that should be addressed in the final camera-ready version of the paper. The authors are encouraged to make the necessary changes to the best of their ability.
Decision: Accept (poster)