Title: Learning Source-Free Domain Adaptation for Visible-Infrared Person Re-Identification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Source-Free Domain Adaptation;  Visible-Infrared Person Re-Identification; Residual Transfer Learning
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

Abstract

In this paper, we investigate source-free domain adaptation (SFDA) for visibleinfrared 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. Code is available at https://github.com/LYXRhythm/SVIP.

1 Introduction

Visible-Infrared Person Re-Identification (VI-ReID) aims to 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 and smart surveillance [1, 2, 3, 4, 5, 6, 7]. By leveraging complementary visual cues from multiple sensors, VI-ReID systems can maintain consistent performance in both daytime and nighttime environments. However, the majority of existing VI-ReID methods rely heavily on large-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-ReID systems.

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

Figure 1

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source domain to an unlabeled target domain [8, 9, 10], achieving notable improvements over purely unsupervised methods, as illustrated in Figure 1. 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 resource-constrained 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 privacypreserving and lightweight model adaptation, aligning more closely with real-world requirements. However, existing SFDA methods are predominantly designed for unimodal tasks and do not generalize well to VI-ReID, where domain shifts and modality gaps must be addressed simultaneously. In the absence of paired visible-infrared samples or annotated target identities, cross-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-invariant and identity-discriminative representations without relying on source data or cross-modal supervision.

Motivated by these observations, we propose a novel learning paradigm, termed S ource-Free Domain Adaptation for V isibleI nfrared P erson 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 a 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-modal 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 crossmodal similar samples within the unpaired target domain, thereby achieving stable and reliable cross-modal feature alignment and further enhancing consistency and robustness of cross-modal representations. Collectively, SVIP effectively transfers discriminative and structural knowledge from 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.

The main contributions of this work could be summarized as follows:

We propose a novel SFDA framework, SVIP, for VI-ReID that adapts a pre-trained source model to an unlabeled target domain without requiring access to any source data. To the best of our knowledge, this is among the first works exploring SFDA specifically for VI-ReID.

SVIP incorporates three key mechanisms: Source Guided Contrastive Learning (SGCL) to enhance supervision reliability via confidence-weighted 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-the-art baselines, validating its effectiveness and superiority.

2 Related Works

2.1 Source-free Domain Adaptation

Source-free domain adaptation (SFDA) has emerged as a crucial paradigm that addresses the limitations of conventional unsupervised domain adaptation (UDA) by eliminating the requirement to access source domain data during the adaptation process [19, 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) Generation-based methods aim to learn domain-invariant representations by synthesizing or transforming 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, 21, 17, 22]. 2) Self-training methods adopt an 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 a 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 as re-identification.

2.2 Domain Adaptation for Person ReID

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 ReID 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 ReID.

3 Methodology

3.1 Problem Statement and Notations

For clarity, we define the key notations and terminology used in this paper. Let Φ S denote the source model trained on a labeled visible-infrared person re-identification (VI-ReID) dataset D S . Additionally, let the unlabeled and unpaired VI-ReID dataset in the target domain be denoted as

Figure 2

Visible / Infrared Instance

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ℒ𝑎𝑎𝑟𝑟𝑑𝑑

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D T = { { x V i } N V i =1 , { x I j } N I j =1 } , where N V and N I are the numbers of visible images x V i and infrared images x I j , respectively. For convenience, we denote D T more generally as D T = { { x P i } N P i =1 } , where P ∈ {V , I} . Due to the domain shift between the source and target domains, the performance of Φ S can degrade significantly in practical scenarios, such as those involving environmental changes (e.g., variations in weather conditions). Our objective is to adapt Φ S to the target domain D T and obtain a target-specific model Φ T , without accessing any source domain samples or ground-truth labels in the target domain. An overview of the SFDA framework for VI-ReID is illustrated in Figure 2. The main motivation of our method lies in the observation that the source model Φ S contains rich discriminative knowledge and cross-modal alignment capabilities. Selectively transferring this knowledge to the target domain can enhance the target model Φ T 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 pseudo-supervision 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 pseudolabel quality and resulting in label noise issues [7, 37, 38, 39, 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 Φ S to guide the target model Φ T towards more reliable supervision signals.

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 Φ T to the target domain D T . First, we extract features of target domain samples using the source model Φ S and the target model Φ T , and apply DBSCAN clustering to obtain two sets of cluster centers, denoted as C S and C T :

To enhance the stability of clustering representations, we introduce two memory banks: M S = { m S k } K S k =1 and M T = { m T k } K T k =1 , which store the cluster center features obtained by the source and target models, respectively, in the current training cycle. Here, K S and K T denote the number

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:

where η ∈ [0 , 1) is the momentum coefficient, and C S k and C T k represent the sample index sets of the k -th cluster for the source and target models, respectively.

Given a sample x P i , we compute its similarity to all cluster centers from both models and obtain a probability distribution over clusters using the Softmax function:

where τ is a temperature parameter that controls the smoothness of the distribution. Then, the prediction confidence is estimated based on the entropy of the distributions, and a fusion weight is computed accordingly:

where p S = [ p S (1) , · · · , p S ( k ) , · · · , p S ( K S )] and p T = [ p T (1) , · · · , p T ( k ) , · · · , p T ( K T )] . H ( · ) denotes the Shannon entropy.

̸

Considering the possible inconsistency in the number of clusters between the source and target models (i.e., K S = K T ), 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 x P i , the optimization objective is defined as:

where m S + and m T + denote the assigned cluster centers for the sample in the source and target domains, respectively. I ( · , · ) represents the InfoNCE loss [42], and N b 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 balancing 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 in 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 a 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.

Specifically, given a target-domain sample x P i , the residual distillation loss is defined as:

L res 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 of 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-modal correspondences poses significant challenges to robust alignment. To alleviate this issue, we propose a 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 potential semantically consistent image pairs, thereby facilitating consistent cross-modal representation learning.

The source model Φ S , 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 cross-modal similarities. Given the unpaired target-domain samples { x V i } N V i =1 and { x I j } N I j =1 , the structural similarity between a visible image x V i and an infrared image x I j is defined as:

where Φ S provides structural prior guidance, and Φ T reflects the adaptation status of the target model. The similarity score S ij integrates source-domain knowledge and target-domain modeling capacity, and is used to construct cross-modal similarity vectors S V→I i = [ S ij | S i 1 , · · · , S iN I ] and S I→V i = [ S ji | S j 1 , · · · , S jN V ] , which are employed to estimate pseudo matching pairs.

To enhance the accuracy and robustness of the pseudo matches, two structural constraints are imposed: 1) Reciprocity Constraint ( C 1 ): Each image pair must be the most similar to each other, i . e ., , Equation (10). 2) Lower-bound Similarity Constraint ( C 2 ): The structural similarity between the image pair must exceed a predefined threshold T , i . e ., , Equation (11).

These constraints are formally defined as:

An image pair ( x V i , x I j ) is regarded as a reliable pseudo match if both C 1 and C 2 are satisfied. Such pairs are marked using a binary indicator A ij = ✶ [( x V i , x I j ) | = C 1 ∧ C 2 ] . 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:

where τ denotes a temperature parameter. This loss employs reliable pseudo matches as supervisory signals and utilizes contrastive learning to enhance semantic consistency between cross-modal 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 Φ T 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 Φ S to guide cross-modal semantic transfer. The optimization objective for the target model integrates three key components and is defined as follows:

where λ 1 and λ 2 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.

Domain Adaptation Settings: 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 ability to handle common domain shifts, including variations in illumination, camera viewpoints, and background complexity. Four adaptation scenarios are considered: (i) SYSU-MM01 → RegDB, (ii) SYSU-MM01 → 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 as a source domain. 2) Weather Setting : To further evaluate model robustness under realistic environmental variations, we introduce five weather conditions to both RegDB and LLCM, including Sunny , Fog , Frost , Rain , and Snow . Each condition is simulated at 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-MM01 is adopted as the source model, and domain adaptation is 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).

Implementation Details: During training, pedestrian images are resized to 288 × 144 pixels. The AGW[47] network is employed as 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 8 × 10 -4 . The initial learning rate is set to 5 × 10 -4 and is reduced by a factor of 10 every 20 epochs. The batch size N b is 128, and training proceeds for 50 epochs. The momentum parameter η is fixed at 0.1, while the temperature hyperparameter τ 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 is maintained at 0.5. The trade-off parameters λ 1 and λ 2 are further analyzed in the Supplementary Material. All experiments are performed on an Ubuntu 20.04 system equipped with four NVIDIA RTX 3090 GPUs.

Training of the Source Model: For the source model, our method could use the model trained by any VI-ReID method. Without loss of generality, we follow [23] and present a simple yet representative approach with the triplet loss function [45, 48] to train a source model for our experiments:

where x a i ∈ D S represents anchor in source training set. x p i ∈ D S and x n i ∈ D S are positive (same identity) and negative (different identity) samples of x a i . τ tri is the margin, which is set to 0.3.

4.2 Comparison with the State-of-the-Arts

We compare our SVIP method with nine state-of-the-art approaches, categorized into two groups. The first group comprises six Unsupervised Visible-Infrared Person Re-Identification (UVI-ReID) methods: OTAL [49], CCLNet [50], PGM [51], GUR [52], SDCL [11], 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 1 and 2, 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-ReID.

4.3 Ablation Study

We conduct comprehensive ablation studies to assess the individual contributions of the key components in SVIP, namely SGCL, RTL, and SCCA, to the overall adaptation performance. Specifically, we progressively remove each component and evaluate the resulting performance degradation on

the target domain. As reported in Table 3, the exclusion of any single component leads to a notable drop in re-identification accuracy, which clearly demonstrates the importance of each mechanism in enhancing domain adaptation.

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 as target domain. As shown in Figure 3, we analyze feature distributions across four configurations using five identities (20 images each) from source and target domains. Based on the experimental results, we can make the following observations: 1) Source Only exhibits severe domain gap with separated source (red) and target (blue) clusters; 2) SGCL partially reduces domain discrepancy through confidence-weighted dual clustering but retains scattered distributions due to insufficient cross-modal alignment; 3) SGCL+RTL enhances structural stability by aligning feature residuals, though noise persists from imperfect sample filtering; 4) The full framework (SGCL+RTL+SCCA) achieves tight cross-domain 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.

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-weighted supervision; 2) a Residual Transfer Learning (RTL) mechanism that distills transferable knowledge through intermediate feature alignment; and 3) a Structural Consistency Guided Cross-modal Alignment (SCCA) mechanism that exploits semantic structure from the source model to guide cross-modal association in 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 ReID), and exploring continual adaptation in streaming settings with evolving target domains.

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; in part by the Chengdu Science and Technology Project under Grant 2023-XT00-00004-GX; and in part by the TCL Science and Technology Innovation Fund.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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.\n\nQUESTIONS:\n1.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.\n\nLIMITATIONS:\nAs 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nThis 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.\n\nQUESTIONS:\nPlease 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n(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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nThis 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.\n\nQUESTIONS:\n1.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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}