LVLM-Driven Attribute-Aware Modeling for Visible-Infrared Person Re-Identification
Authors: Zhiqi Pang
Lingling Zhao
Junjie Wang
Chunyu Wang
Venue: NeurIPS 2025 poster ()
Keywords: visible-infrared person re-identification
large vision-language model
unsupervised learning
contrastive learning
Primary area: applications
TL;DR: We enhance visible-infrared person re-identification by leveraging attribute arrays extracted using a large vision-language model.

Abstract: Visible-infrared person re-identification (VI-ReID) aims to match visible and infrared images of the same individual. Supervised VI-ReID (SVI-ReID) methods have achieved promising performance under the guidance of manually annotated identity labels. However, the substantial annotation cost severely limits their scalability in real-world applications. As a result, unsupervised VI-ReID (UVI-ReID) methods have attracted increasing attention. These methods typically rely on pseudo-labels generated by clustering and matching algorithms to replace manual annotations. Nevertheless, the quality of pseudo-labels is often difficult to guarantee, and low-quality pseudo-labels can significantly hinder model performance improvements. To address these challenges, we explore the use of attribute arrays extracted by a large vision-language model (LVLM) to enhance VI-ReID, and propose a novel LVLM-driven attribute-aware modeling (LVLM-AAM) approach. Specifically, we first design an attribute-aware reliable labeling strategy, which refines intra-modality clustering results based on image-level attributes and improves inter-modality matching by grouping clusters according to cluster-level attributes. Next, we develop an explicit-implicit attribute fusion module, which integrates explicit and implicit attributes to obtain more fine-grained identity-related text features. Finally, we introduce an attribute-aware contrastive learning module, which jointly leverages static and dynamic text features to promote modality-invariant feature learning. Extensive experiments conducted on VI-ReID datasets validate the effectiveness of the proposed LVLM-AAM and its individual components. LVLM-AAM not only significantly outperforms existing unsupervised methods but also surpasses several supervised methods.

LVLM-Driven Attribute-Aaare Modeling for Visible-Infrared Person Re-Identification
Zhiqi Pang'
Lingling Zhao'
Junjie Wang²
Chunyu Wang'*
Harbin Institute of Technology, China
Nanjing Medical University, China
zapang98ggai11.com
zhaoll@hit. edu. junjichit@163.co com, chunyughit edu.
Abstract
Visible-infrared person re-identification (VI-ReID) aims to match visible and in- frared images of the same individual. Supervised VI-ReID (SVI-ReID) methods have achieved promising performance under the guidance of manually annotated identity labels. However, the substantial annotation cost severely limits their scala- bility in real-world applications. As a result, unsupervised VI-ReID (UVI-ReID) methods have attracted increasing attention. These methods typically rely on pseudo-labels generated by clustering and matching algorithms to replace manual annotations. Nevertheless, the quality of pseudo-labels is often difficult to guar- antec, and low-quality pseudo-labels improvements. To address these challenges, we explore the use attribute arrays extracted by a large vision-language model (LVLM) to enhance VI-RelD, and propose a novel LVLM-drive attribute-aware modeling (LVLM-AAM) approach. Specifically. first design attribute-awee reliable labeling strategy. which fines intra-modalle clustering results based on image-level attributes and improves inter-modality matching by grouping clusters according to cluster-level attributes. Next, we develop an explicit-implics attribute fusion module, which integrates explicit and implicit attributes obtain more fine-grained identity-related text fea- tures. Finally, we introduce an attribute-aware contrastive learning module, which jointly leverages static and dynamic text features promote modality-invarian feature learning. Extensive experiments conducted on VI-ReID datasets validate the effectiveness of the proposed LVLM-AAM and its individual components. LVLM-AAM not only significantly outperforms existing unsupervised methods but also surpasses several supervised methods.
significantly hinder model performance
Introduction
35 focuses on identifying images of specific person from a large-scale gallery. To advance intelligent surveillance systems across various time periods, visible-infrared ReID (Vi-ReID) 31 visible and infrared images of the same person. While supervised VI-ReID (SVI-ReID) methods 43 (7 have shown promising performance on multiple datasets, they heavily rely manually annotated identity labels in the training set. However, manually annotating data for VI-ReID tasks is an extremely inteniitee process. As result, increasing attention has been given to unsupervised
Person re-identification (ReID) 45 2
57
34
25
10
4o
42
22 g
was introduced, aiming to match
33 These UVI-RelD methods typically begin by clustering image features extracted by an image encoder to generate intra- aitra-modlltty pseudo-labels. They then perform inter-modality matching generate inter-modality pseudo-labels, These intra- modality and inter-modality pseudo-labels serve supervisory signals to replace manual labels, thus reducing the limitations imposed by the high cost of annotation.
VI-RelD (UVI-RelD) 21
38 32
36
29
"Corresponding author: Chunyu Wang
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

vicllle stoble cluder iigoare
awatcing
maitsingg
Youile
Fonile festores
BITESS
Pustle Maggor
optinice
Flahle INagEe
Optiante
Image
Aiii of Aid (8l) (Ea pame
Tams
Image Fneoder
Frasiee plaas Jann
Encoder
lettoe image
Locherr plases barrec
opttnax
Jsharrll Imsage
Infrared Jesturer
Jeharad
Infrared
(a) Existing methods
LVLM-AAM
Figure 1: The differences between existing methods and LVLM-AAM are highlighted with red lines. Different feature colors indicate different pseudo-labels. (a) The texts are entirely derived from pseudo-labels without enhancing the quality of the aseudo-labels. Moreover, these texts are only utilized for optimization within their corresponding modality. (b) LVLM-AAM leverages attribute arrays provided by LVLM to simultancously enhance intra- intra-modality pseudo-labels, inter-modality matching (pseudo-llbees), and texts. Furthermore, the text semanties can be mutually transferred between modalities during the optimization process. We define correct match as clusters from different modalities that contain images of the same identity, and an incorrect match otherwise.
However, the quality of pseudo-labels generated by clustering algorithms is largely constrained by the performance of the pretrained image encoder, and the global-level inter-modality matching often leads to mutual interference, resulting in cascading errors (32. Although CLIP-based UVI-ReID methods [3] attempt to leverage the pretrained CLIP model to obtain text semantics as additional supervision signals, as shown in Figure D two critical limitations still remain: (1) The texts are constructed based on pseudo-labels generated by clustering algorithms, thus inherently carrying similar supervision signals without fundamentally improving the pseudo-labels. (2) This method typically utilizes text semantics only to optimize features within the corresponding modality, focusing on enhancing muta-modality identity discrimination, but without explicitly assisting the image encoder in learning modality-invariinn features.
Inspired by the powerful fine-grained vision-language understanding capability of the large vision- language model (LVLM) M. we attempt to leverage attribute arrays extracted by an LVLM to improve VI-ReID, as illustrated in Figure Dp. We propose novel LVLM-driven attribute-aware modeling (LVLM-AMM) method address the aforementioned two problems. To tackle the first issue. we first employ an LVLM to extract attribute arrays (Gender, Upper, Lower, Glasses, and Backpack) for each image in the training set, which are referred to as explicit attributes in the following descriptions. Then, we design an attribute-aware reliable labeling (ARL) strategy, which consists of attribute-awas aware refinement (AR) and attribute-aware matching (AM). Specifically, AR refines intra-modality clustering results based on image-level attribute arrays, while AM groups clusters based on cluster-level attribute arrays to enhance inter-modality matching. Subsequently, we develop explicit-implict attribute fusion (EAF) module, which fuses implicit attributes (text embeddings) and explicit attributes to obtain more fine-grained identity-related text features. To address the second issue, we further propose attribute- contrastive learning (AAC), which not only computes dynamic text features based on static text features, but also optimizes with both static and dynamic features to enhance modality-invarinn feature learning.
worth noting that, since LVLM-AAM leverages supervision information (i.e., attribute arrays) extracted from an LVLM, it may not be considered fully UVI-RelD method. In other words, the primary goal of this work is to explore the effectiveness of utilizing an LVLM to advance the practical application of UVI-ReID specifically, to improve recognition performance while maintaining low manual annotation costs. The contributions of this work are summarized as follows:
We explore the use of attribute arrays extracted by an LVLM to improve VI-ReID and propose a novel LVLM-AAM method, which leverages attribute arrays to refine pseudo- labels and text semantics for enhanced modality-invarian feature learning.
We design an ARL strategy and an EAF module. The former refines intra-modality and inter-modality pseudo-labels based on image-level and cluster-level attributes, respectively, while the latter utilizes attribute arrays generate fine-grained text features.

---

We develop an AAC module, which computes dynamic text features based on static text features from both modalities, and optimizes with both static and dynamic features to further enhance modality-invariant learning.
Extensive experiments conducted on VI-ReID datasets validate the effectiveness of the proposed LVLM--AM and its components. LVLM-AAM not only significantly outperforms existing unsupervised methods but also surpasses several supervised methods.
2 Related Work
Infrared Person Re-ldentification. Given an infrared image of a person, VI-ReID Ki
46 42
aims to retrieve the corresponding visible image from large-scale gallery, and vice versa. Early studies primarily focused on the supervised VI-ReID (SVI-ReID) [43] 5 [3] setting, where manual annotations were used to guide the learning process and reduce the impact of modality gap. Although SVI-ReID methods have demonstrated promising recognition capabilities, they are constrained by the high cost of manual annotations. As a result, increasing attention has been directed toward the unsupervised VI-RelD (UVI-ReID) setting. For instance, H2H [21] as one of the early UVI-RelD approaches, first pretrains the image encoder on a manually labeled visible dataset (54), and then performs unsupervised learning visible-infrared dataset. ADCA (38 further eliminates the reliance on manual annotations for pretraining by first conducting homogeneous learning within each modality, followed by heterogeneous learning through inter-modality matching. Building upon ADCA, PGM [32) introduces a graph matching strategy to globally establish inter-modality positive clusters. Among recent methods, SDCL (37] enhances model optimization by exploring the relationships between shallow and deep features within the Transformer architecture [5] [4]. providing abundant supervisory signals. PCLHD [30] introduces hard prototypes supply diverse supervisory signals for optimization. Although existing UVI-RelD approaches have made significant efforts to obtain diverse and reliable supervision, these methods primarily rely image feature distances or similarities. In contrast to previous work, we go beyond purely image- based features by leveraging attribute arrays extracted by the LVLM as additional supervisory signals improve VI-ReID performance.
Vision-Language Models. Powered by large-scale pretraining, vision-language models (VI.Ms) [6] have demonstrated strong vision-language understanding capabilities and achieved competi- tive performance across various downstream tasks in computer vision. CLIP (27). representative work in the VLMs domain, typically consists of an image encoder and a text encoder. Given an input image-text pair, CLIP (27] aims predict their similarity. Subsequent research, such as CoOp (56), enhances the flexibility of CLIP [27] by learning a set of task-specific text embeddings for each image category in downstream tasks. Inspired by the success of VLMs, researchers in the ReID community have also begun exploring VLMs- approaches. For instance, CLIP- ReID (8] leverages text semanties by learning set of text embeddings for each identity assist the image encoder in extract- ing identity-related features. TVI-LFM [15] utilizes text descriptions of visible images to augment the corresponding infrared images of the same identity, thereby improving visible-infrared retrieval performance, In contrast to the aforementioned supervision methods, CCLNet [3] in the UVI-ReID field learns set of text embeddings for each intra-modality pseudo-label obtained by clustering, and uses the optimized text embeddings as supervisory signals to enhance intra-cluster compactness. However, existing UVI-RelD approaches face two key limitations: (1) the optimized text embeddings provide supervision signals similar to the original Leeudo,labels, without substantially improving the quality of the pseudo-labels; (2) the optimized text embeddings are solely used to promote intra-modality identity learning, without explicitly assisting the model in learning modality-invarian features. To address these issues, our proposed I.VLM-AMM method leverages attribute arrays to refine intra- modality and mnter-modality pseudo-labels and jointly utilizes both explicit and implicit attributes to promote modality-invanl innaaiitn feature learning.
3
The Proposed Method
3.1 Task Formulation and Method Overview
In the UVI-ReID task, we are provided with an unlabeled training set consisting of visible image set (2 and an infrared image set IEz N Our goal is to train an image encoder capable of
Ji=1
3

---

AR
EAF
Chewewallr Redlaed mealle
inngee of 5i
CLIP Text Encoder
Tout
CLIP Image Encoder
Pinrlle
Pluible
Plase "waleet!
Lox
Encoder
Image
AM
Ourrynn"
Blmil alrilutt Cumrynng "dar (dene]
Pigter
AAC
Lowe
Lap
Lynte
image of's (al
CLIP Text Eacodar
CLIP Imaag Encodar
fentures
Infeared
Refiaed
EAF
hartssggotthaaa luckzack
Texts
Teat fratters
Hoatorey
Infoamed
Inforeal
Alasic infrumed lexa, festtoony
Jynumic rejiared far fiuttrrss
virible bant foutterss
Jynamic featurer
refoared fenthrres
virible Sentures
visible choiter
Infared claster
rellaed
Figure 2: Illustration of our LVLM-AAM. The different colors of the features represent different pseudo-labels, while different shapes denote different modality labels.
extracting modality invariian and Srentity-discrmiinee
features. Before optimizing the image
encoder, we utilize an LVLM [ to extract a five-value attribute array for each image in {=ā] J= and
{ur We Specifically, we first prompt the LVLM [ to respond in the following format: "Gender:
[male/female]]" "Glasses: [wearing/witthouj]]" "Backpack: [carrying/without]] "Upper: [clothing type]," and "Lower: [clothing type]." The detailed prompt can be found in Supplementary Material Section S.I. Subsequently, we arrange the extracted attribute values in the order of Gender, Glasses, Backpack, Upper, and Lower, obtaining a five-value attribute array for each image (e.g., ['male", "without"" "carrying". "shirt", "shorts"J). In the following descriptions, we refer to these arrays as explicit attributes to distinguish them from implicit attributes (i.e., text embeddings).
As shown in Figure 2 the proposed LVLM-drived attribute-ave aware modeling (LVLM-AAM) method first employs an attribute-aware reliable labeling (ARL) strategy, which consists of an attribute- aware refinement (AR) module and attribute-aware matching (AM) module, obtain reliable intra- modality and inter-modality pseudo-labels, respectively. Meanwhile, an explicit-implice attribute fusion (EAF) module is introduced, which leverages both explicit and implicit attributes along with pretrained CLIP (27] model consisting of text encoder and an image encoder to generate fine-grained text features. Finally, LVLM-AAM adopts an attribute-aware contrastive learning (AAC) module to generate dynamic text features, thereby guiding the image encoder to learn Sientity-discriminge and modality-invarian features.
3.2 Attribute-Aware Reliable Labeling
Attribute-waare Refinement. We first input the visible image set [x Ji=l and the infrared image set {x]]) into the image encoder to obtain the visible feature set [ Ji=1 and the infrared feature set (.T) Ji=1- Then, we apply the DBSCAN algorithm 6 to perform clustering within both the visible and infrared modalities to obtain intra-modality pseudo-labels. Next, for any given cluster (pseudo-label), determine the mode of attribute array values at each position across all attribute arrays within the cluster. each cluster, these five mode values are then aggregated to formulate cluster-level attribute array. From the perspective of ensemble learning [7 411. the mode-based cluster-level attribute array better reflects the overall characteristics of the cluster than a single image-level attribute array. Therefore, within the cluster by excluding those that deviate significantly from the cluster-level attribute array. Specifically, we identify image-level attribute arrays that differ from the cluster-level attribute array by more than values as outliers and exclude the corresponding images. Ultimately, we obtain the refined clustering results (pseudo-labels), which are used for intra-modality contrastive learning.
the cluster-level attribute array to refine the images
Attribute-Aware Matching. Existing inter-modality matching methods [32 3 30 typically per- form matching at global level. However, global cluster matching often leads to cascading errors due to mutual interference among clusters. Fortunately, the introduction of cluster-level attributes provides a foundation for more fine-grained inter-modality matching. Specifically, we group clusters based on the first three attribute values in their cluster-level attribute arrays. For example, clusters

---

with the first three attributes as "male," "without"" and "carrying" are grouped together. while those with "male," "wearing," " and "carrying" are placed in a different group. this work, we obtain total of eight groups, with each group containing both visible and infrared clusters. During the moder-modality matching process, we perform progressive graph matching [22 within each group to generate inter-modality pseudo-labels. The detailed matching process is provided in Supplementary Material Section S.II
We use all attributes in the attribute-aware refinement module for outlier detection to align with realistic perceptual principle: evaluating an object from more dimensions (attributes) is generally more accurate and comprehensive than doing so from fewer dimensions. We restrict attribute-an aware matching the first three attributes because they have countable value spaces, which ensures that each group contains both visible and infrared clusters.
3.3 Intra-Inter Modality Contrastive Learning
undra-Modality Contrastive Learning. Within each modality, we compute the cluster centers based the refined clustering results. For example, the center of the p-th cluster in the visible modality is defined as:
N
f
(I)
where f." denotes an image feature within the cluster, and N2 represents the number of images in the cluster. The center of the p-th cluster in the infrared modality, denoted as cp is computed a similar manner. Finally, we introduce the intra-modality contrastive loss encourage the image encoder to learn identity-discrimnaati features. For example, for any image feature S in the visible modality, the intra-modality contrastive loss is defined as:
exp(Si c///T) exp(S; T/T/T)"
Dintra = - loog E
where c₁ denotes the center of the cluster to which f." belongs, C" is the total number of clusters in the visible modality the current epoch, and is the temperature hyperparameter. The intra-modality contrastive loss for the infrared modality, denoted as L is computed in a similar manner. Thus, the total intra-modality contrastive loss is defined
Tintra Lnntro
Attribute-ane aware refinement and intra- mntra-modality contrastive learning are iteratively performed, and the final intra-modality pseudo-labels preserved.
Inter-Modality Contrastive Learning. We optimize the image encoder based on the inter-modality pseudo-labels (matches) obtained from attribute- aware matching. For example, for any visible feature f." the inter-modality contrastive loss is defined as:
explf" cT /T) E exp(SI 1r /T)
Linter log C
where cf denotes the center of the infrared cluster matched to the cluster which f belongs, C" is the number of clusters in the infrared modality at the current epoch, and is a temperature hyperparameter. Similarly, the moter-modality contrastive loss L, for infrared features can be defined in the same way. Following the alternate cross contrastive learning scheme [32, the overall inter-modale modaliyy contrastive loss is defined as:
Linter Linter:
(5)
Lntter: epoch%2 = =1
where epoch represents the index of the current epoch.
5

---

3.4 Explicit-Implicit Attribute Fusion
After the iterative execution of attribute-aware refinement and intra-modality contrastive learning, we assign to each cluster a text containing learnable text embeddings "[Xi]
[XM]" in [X1] [X2] .2)...... [X [male/ ['mmale] [wearing withoul) glasses and [carrying/ /without) backpack." Here, M represents the number of learnable text embeddings, and [male/ / female]. [wearing/ peroriig/wwhhot//// without), and (carryingwwithoutt /without] correspond to the first three attribute values from the cluster-level attribute array of the respective cluster. By incorporating both explicit and implicit attributes, we enrich the semantic content of the text.
the format "An image
Subsequently, we input the image and its corresponding text into the pretrained CLIP [27] image and text encoders, respectively, obtain the image features fg and text features t.. Following existing optimization strategies, we freeze the pretrained CLIP [27] image and text encoders and introduce the CLIP contrastive loss to optimize the learnable text embeddings:
Leip = Lizt + Le25s
(6)
exp(J² . 127)) Liz log
(7)
Σg exp(S² =tz1)²
T22:
1
Σ log S;EP
explt, fz
(8)
Ps
Σ exp(ly ƒĄ
where E {e, r denotes the index for the visible or infrared modality, and JØ and 1, represent the image and text features with the same pseudo-label. B refers to the batch size, and Pe is the set of image features in the batch that share the same pseudo-labes as ty. Finally, we refer to the converged text features the static text features.
3.5 Attribute-Aware Contrastive Learning
Although the static text features contain identity-related semantic information, they are modality- dependent. Therefore, to leverage the text features for promoting modality-invand invariant learning, we first obtain dynamic text features based on the static text features following sttribute-wwar matching. For example, for the p-th cluster in the visible modality, its dynamic text feature is defined as:
? = (1-a)) + af
where E and to represent the static text features of the cluster and its matching cluster in the infrared modality, respectively. a is the weight hyperparameter. The dynamic text features in the infrared modality are computed using similar approach. The dynamic text feature incorporates information from both the visible and infrared modalities, and thus tends to be more to the static text features. Moreover, since the dynamic text feature is derived from two static text features that share the same inter-modality pseudo-label, it also retains identity-related information.
compared
Subsequently, we introduce a text semantic contrastive loss to promote modality-invarian learning. For any image feature fø. the text semantic contrastive loss defined as:
exp(S$ -1:))
Lisc
log Σ Egeerrui exp(Sī 18
(10)
where ig represents the dynamic text feature corresponding to fp. and Q' denotes the set of all static text features in the modality which fo belongs.
In summary, the total loss used to optimize the image encoder is defined as:
Liotal
Lintra + Xinter Linter + Atse
(11)
where Xinter and Arsc are the weight hyperparameters for Tinter and Tases respectively. The overall algorithmic procedure is provided in Supplementary Material Section S.III.

---

4 Experiment
4.1 Datasets and Evaluation Metrics
We evaluate our method on the SYSU-MM01 31 RegDB 23 and LLCMM 52 datasets. SYSU- MM01 consists of images from 491 identities captured by four visible cameras and two near-infrared cameras. Following existing methods [3 a total of 22,258 visible images and 11,909 infrared images from 395 identities are used for training. The query set and gallery consist of infrared and visible images, respectively. from the remaining 96 identities. RegDB contains 412 identities, with each identity having 10 visible images and 10 thermal infrared images. Following existing protocols 3 37,, we use images from 206 identities for training and the remaining 206 identities for testing. LLCM is collected under complex low-light conditions, making it more challenging dataset compared to the previous two. It contains 46, 7,7 bounding boxes of 1,064 identities captured by 9
cameras.
Following existing methods [3 37
we use cumulative matching characteristics (CMC), mean average precision (mAP), and mean inverse negative penalty (mINP) to evaluate performance.
4.2 Implementation Details
The image encoder of LVLM-AAM is based on a pretrained ResNet-50 1 and consists of two branches to separately handle inputs from the visible and infrared modalities. For the learnable text embeddings, we set M 4. All images are resized to 288x144, and random flipping, random grayscale conversion [19] channel augmentation [47]. and random erasing (55] are applied as data augmentation. We set the batch size B to 128. In each iteration, we randomly select clusters from each modality, and sample 16 images from each cluster. We use DBSCAN [6] to perform intra-modality clustering, where the distance threshold and the minimum number of samples are set to 0.6 and 4, respectively, on SYSU-MM01 [1I, and to 0.3 and 4 on RegDB (23. We adopt the Adam optimizer [16] for model training. Homogeneous learning (i.e., Eq. (3) is performed for 50 epochs, followed by an update of the learnable text embeddings (i.e., Eq. 6 over another 50 epochs. Finally, heterogeneous learning (i.e., Eq. TT is conducted for an additional 50 epochs. The initial learning rate is set to 0.00035, and it decays 10 times every 20 epochs. The temperature hyperparameter is set to 0.05. For attribute-aware refinement (AR), we set rj = 2. For attribute-aware contrastive learning (AAC), we set a 0.5. Regarding the weight hyperparameters for Linter and Liecs we set Ainter = 0.5 and Atac 0.5. An analysis of the sensitivity of LVLM-AAM to the hyperparameters and Atse can be found in the Supplementary Material Section S.IV. The experiments are conducted on four NVIDIA GeForce RTX 4090 GPUs. The LVLM inference is only required once during the training phase to extract identity attributes and not needed during the testing phase. Therefore, the computational cost associated with LVLM inference does not affect the inference speed of the trained ReID model during testing.
4.3 Comparison with the State-of-the-art Methods
As shown in Table we compare the proposed LVLM-AMM with existing methods on SYSU- MM01 (both All Search and Indoor Search) and RegDB (Visible to Thermal). Among existing UVI-ReID methods, SDCL (37] and DLM [48] have achieved strong performance on SYSU-MMO1 and RegDB, respectively. Our proposed LVLM-AAN surpasses both SDCL (37] and DLM overall performance across both datasets. Specifically, on SYSU-MM01 (All Search), LVLM-AAM outperforms SDCL (37) by 2.09%, 0.26%, and 1. 1.23% in terms of Rank-1 accuracy, mAp, and mINP, respectively. On Reg DB (Visible to Thermal), L achieves improvements of 2.70%, 1.13%, and 2.05% over DLM 48 on the same three metrics. This mainly because LVLM-AAM not only effectively leverages attribute arrays provided by the LVLM to obtain reliable pseudo-labels, but also jointly utilizes explicit and implicit attributes to further promote modality-invariant feature learning. SVI-RelD methods rely on manually annotated identity labels, which are inaccessible to LVLM-AAM and require significantly higher human effort compared to the attribute arrays used by LVLM-AAM. Encouragingly, LVLM-AAM achieves superior overall performance on both datasets compared to early SVI-ReID methods (e.g., DDAG [44), AGW (45). and MCLNet [12), and demonstrates competitive results against more recent methods (e.g., FMCNet [51] and DART R.)). Moreover, on the RegDB dataset, the Rank-1 accuracy of L.V.M-AAM already comparable to that of the
48] in
latest SVI-ReID methods (c.g., SAAI 7 and STDR (26)). These results further validate the
7

---

Table I: Comparison with state-of-the -fe-----t-hrrrr art methods the SYSU-MM01 and RegDB datasets. The best performances among UVI-RelD methods are highlighted in bold, while performances SVI-
RelD methods that lower than those of LVLM-AAM are indicated in italics.
SYSU-MMO1
RegDB
Methods
All Search
Indoor Search
Visible to Thermal
Rank-1
mAP
mlNP
Rank-1
mAP
mlNP
Rank-1
mAP
mlNP
H2H 21
30.15
29.40
23.81
18.87
ADCA
38
45.51
42.73
28.29
50.60
59.11
55.17
67.20
64.05
52.67
CHCR 24
47.72
45.34
69.31
64,74
UVI-ReID
CCLNet
3
54.03
50.19
56.68
65.12
69.94
65.53
PGM 32
57.27
51.78
34,96
56.23
62.74
58.13
69.48
65,41
GUR 36
63.51
61.63
47.93
71.11
76.23
72.57
73.91
70.23
58.88
SDCL
64.49
63.24
51.06
71.37
76.90
73.50
86.91
78.92
62.83
PCLHD
30
64.4
58.7
69.5
74.4
84.3
80.7
PCAL
40
57.94
52.85
36,90
60.07
66.73
62.09
86.43
82.51
72.33
DLM 48
62.15
58.42
43.70
67.31
72.86
68.89
87.55
82.83
71.93
DDAG
44
54.75
53.02
39.62
61.02
67.98
62.61
69.34
63.46
49.24
AGW
47.50
47.65
35.30
54.17
62.97
59.23
70.05
66.37
50.19
MCLNet
65.40
61.98
47.39
72.56
76.58
72.10
80.31
73.07
$7.39
SVI-ReID
FMCNet
66.34
62.51
68.15
74.09
89.12
84.43
DART
39
68.72
66.29
53.26
72.52
78.17
74.94
83.60
75.67
60.60
SGIEL 8
77.12
72.33
82.07
82.95
92.18
86.59
MUN 49
76.24
73.81
79.42
82.06
95.19
87.15
SAAI
75.90
77.03
83.20
88.01
91.07
91.45
IDKL
81.42
79.85
87.14
89.37
94.72
90.19
STAR-RelD RelD
26
82,93
80.47
88.04
89.58
91.89
93.31
LVLM-AAM
66,58
63.50
52.29
72.97
78.65
75.21
90.25
83.96
73.98
Table 2: Comparison with -fet---rrrr methods on the LLCM dataset.
Visible to Infrared
Infrared to Visible
Methods
Reference
Rank-1
mAP
Rank-1
mAP
CCLNet 3
MM'23
45.3
49.9
39.3
45.3
PGM 32
CVPR'23
44.4
48.6
38.4
44.2
SDCL 37
CVPR '24
46.9
52.4
43.4
48.2
SCA-RCP 20
TKDE'24
29.1
33.3
22.3
28.0
LVLM-AAM
Ours
52.2
57.3
46.0
51.7
effectiveness of LVLM-AAM and highlight the potential of replacing costly manual annotations with automatically extracted attribute arrays.
We further compare LVLM-AAM with state-of-the-arr unsupervised methods the LLCM dataset. As shown in Table 2 our method outperforms existing methods in both testing scenarios of LLCM. For instance, compared to SDCL (37), LVLM-AAM achieves
significant performance gain LLCM than on SYSU-MMO1. Moreover, despite SCA-RCP [20] utilizing camera labels, LVLM- AAM still demonstrates substantial advantage. This is because LLCMM presents greater complexity compared to SYSU-MM01 and RegDB, making g more challenging for existing methods to obtain reliable pseudo-labels. In contrast, I.V.M-AAM effectively leverages attribute arrays from the LVL.M to enhance the reliability of pscudo-labels and utilizes text semantics to facilitate model optimization, thereby achieving superior performance over existing methods. These experimental results not only validate the superiority of the proposed LVLM-AAM but also demonstrate its strong generalization capability.
4.4 Ablation Study
In this section, we evaluate the effectiveness of attribute-awai refinement (AR), attribute-awan matching (AM), sxplicit-implici attribute fusion (EAF), and attribute- contrastive learning (AAC). As shown T the baseline adopts the same image encoder and CLIP-based architecture as I.V.M-AAM. The key difference lies in that the baseline does not incorporate AR, AM, or EAF, and replaces AAC with the image-to-text contrastive loss (ITC) K. which utilizes text features containing only implicit attributes to assist the optimization of the image encoder. Four ablation

---

Table 3: Ablation study the SYSU-MM01 and RegDB datasets.
SYSU-MMOIAAI Search)
RegDB Visible to Thermal)
Methods
AR
AM
EAF
ITC
AAC
Rank-
mAP
mINP
Rank- 1
mAP
mINP
Baseline
58.52
52.89
35.27
72.55
68.59
56.90
A1
60.85
56.03
41.26
75.63
71.28
60.05
A2
61.36
57.40
43.18
78.34
73.57
62.17
A3
V
62.51
59.26
46.11
80.29
75.16
65.23
A4
V
64.59
61.89
50.69
86.57
79.85
70.29
A5
66.58
63.50
52.29
90.25
83.96
73.98
0.25
1.00
36
1,5
0.98
MWN 22107
P-ggore
F-score
0.96
0.19
1.3
0.94
Npp
0.17
Reeelin
0.92
Besalin::
24
A1
1.2
A1
0.15
10
15
20
25 30
35 40 45 50 Epochs
0.90
5
10
20
10
15
20
25
30 Epochs
35
1.1
5
Epochs
Epochs
(a) F-score (SYSU-MMO1)
(b) F-score (BegDB)
(c) NIPP (SYSU-MMO1)
(d) NIPP (RegDB)
Figure 3: Statistical results of F-score and NIPP.
variants (AI, A2, A3, and A4), along with the complete LVLM-AAM (A5), progressively introduce AR, AM, EAF, and AAC based on the baseline.
Effectiveness of AR and AM. As shown in Table 3 introducing AR in A1 leads to noticeable performance improvements over the baseline on both SYSU-MM01 and RegDB. For example, on SYSU-MM01 (All Search). Rank-1, mAP, and mINP are improved by 2.33%, 3.14%, and 5.99%, respectively, while on RegDB (Visible to Thermal), the three metrics are increased by 3.08%, 2.69%, and 3.15%, respectively. To further evaluate the effectiveness of AR, assess the F-score [T]] of the intra-modality pseudo-labels. A higher F-score (IT] indicates greater accuracy of the pseudo-labele As shown in Figure Ba and Figure 3b) Al achieves significantly higher F-score compared to the baseline, verifying that AR can effectively leverage attribute arrays to enhance the reliability of pseudo-labels and thereby improve model performance. Building upon AI, A2 introduces AM and achieves further performance improvements. For example, on SYSU-MM01 (All Search), Rank-1, mAP, and mINP increase by 0.51% 1.37%, and 1.92%, respectively. On RegDB (Visible Thermal), the three metrics improve by 2.71%. 1,, 2.29%, and 2.12%, respectively. In addition, we analyze the number of inter-modality positive pairs (NIPP) obtained by Al and A2. Specifically, two images are considered an mnter-modality positive pair if they share the same inter-modality pseudo-label, the same ground-truth identity label, and different modality labels. Generally, a higher NIPP indicates more accurate moter-modality matching. As shown in Figure 33 and Figure [3 A2 increases NIPP compared to Al. This confirms that AM can enhance model performance by improving the accuracy of inter-modality matching.
Effectiveness of EAF and AAC. EAF introduces explicit attributes to enrich the text semantics. As shown in Table 3, A3 incorporates EAF based on A2 and achieves further performance improvements on both MMM0 and RegDB. In the VI-ReID task, due to the modality gap, the feature distance between inter-modality positive pairs is typically much larger than that between intra-modality positive pairs. As shown in Figure 4 compared to A2, A3 slightly reduces the feature distance between inter-modality positive pairs. This improvement can be attributed to EAF introducing explicit, modality-invariinn and identity-relvaan attributes to enrich the text semantics, thereby effectively enhancing modality- -nnvariant feature learning. A4 builds upon A2 by introducing AAC, which involves computing dynamic text features and replacing the image- to-text contrastive loss (ITC) 3 with the text semantic contrastive loss (Eq. [O). As shown in Table 3 A4 achieves significant performance improvements over A2 after introducing AAC. For example, on SYSU-MM01 (All Search), Rank-1, mAP, and mINP improve by 3.23%. 4.49%, and 7.51%, respectively. As illustrated in Figure 4 A4 significantly reduces the feature distance between inter-modality positive pairs compared to A2. This is because AAC incorporates inter-modality matching information into the

---

Relative Frogarn
Rolative Frogokccy
Reletive Freenaoyy
Relative Frequency
inter
inter
inter
?
inter
+.
0.0
0.2
0.4 Distance
0.6
0.8
0.2
01.4 Distance
0.6
0.2
01.5
0.6
0.8
0.2
0.4 Distance
0.6
0.5
Distance
(a) A2
(b) A3
(c)
(d) LVLM-AAM
Figure 4: The feature distance distributions of intra-modality and inter-modality positive pairs for A2,
A3, A4, and LVLM-AAM.
dynamic text features and encourages image features approach the dynamic text features during optimization, thereby enhancing modality-invarian feature learning.
Furthermore, observe that LVI.M-AM (A5) outperforms all of the aforementioned ablation variants and further reduces the feature distance between inter-modality positive pairs compared to A3 and A4. This validates that AR, AM, EAF, and AAC can be organically integrated enhance the model's performance in cross-modality scenarios.
5 Conclusion and Limitations
In this paper, we propose an LVLM-driven attribute-aware modeling (LVLM-AAM) method to improve VI-ReID. Ablation studies validate the effectiveness of each module in AVLM-AAM. Specif- ically, attribute-awar reliable labeling, which comprises attribute-an aware refinement and attribute- aware matching. effectively leverages attribute arrays to enhance the reliability of both intra-modality and inter-modality pseudo-labels. Explicit-implici attribute fusion utilizes attribute arrays to ac- quire fine-grained identity-related text features, while attribute-aware contrastive learning promotes modality-invariant learning by integrating static and dynamic text features. Comparative experimental results demonstrate the superiority of LVLM-AAM, which not only significantly outperforms existing unsupervised methods and earlier supervised approaches but also competes with state-of-the- supervised methods in certain scenarios.
Essentially, this paper represents an early exploration of applying the LVLM to UVI-ReID, with the core contribution being the preliminary validation of the effectiveness of attribute arrays extracted by the LVLM in UVI-ReID. However, a thorough analysis of a broader range of attribute arrays has not been conducted, which could serve potential direction for future research. Additionally, the eight groupings manually set in attribute-aware matching are a very preliminary exploration, and exploring more diverse flexible strategies in the future could yield even more promising results.
Acknowledgements
This work is supported by National Key Research and Development Program of China (Grant no. 2023YFC3305003, 2023YFC3305000
References
[1] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang. Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint 1392:22502.13923. 2025.
[2] Hao Chen, Benoit Lagadec, and Francois Bremond. Ice: Inter-instance contrastive encoding for unsupervised person re-identification. ICCV, pages 11960-14969, 2021.
[3] Zhong Chen, Zhizhong Zhang, Xin Tan, Yanyun Qu, and Yuan Xie. Unveiling the power of clip
in unsupervised visible-infrared person re-identification. In ACM MM, pages 3667-3675, 2023.
[4] Zuozhuo Dai, Guangyuan Wang, Weihao Yuan, Siyu Zhu, and Ping Tan. Cluster contrast for unsupervised person re-identification. In ACCV, pages 1142-1160, 2022.
10

---

[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICI.R, 2021.
[6] Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD, volume 96, pages 226-231, 1996.
[7] Xingye Fang, Yang Yang, and Ying Fu. Visible-infrared person re-identification via semantic alignment and affinity inference. In ICCV, pages 1 11270-11279, 2023.
[8] Jiawei Feng, Ancong Wu, and Wei-Shi Zheng. Shape-erased feature learning for visible-infrared person re-identification. In CVPR, pages 22752 227611, 2023.
[9] Zhanxiang Feng, Jianhuang Lai, and Xiaohua Xie. Learning mppecit representations for visible-infrared person re-identification. IEEE TIP, 29:579-590, 2019.
[10] Yunpeng Gong, Zhun Zhong, Yansong Qu, Zhiming Luo, Rongrong Ji, and Min Jiang. Cross- modality perturbation synergy attack person re-identification. NeurlPS, 2024.
[11] Cyril Goutte and Eric Gaussier. A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. ECIR, pages 345-359. Springer, 2005.
[12] Xin Hao, Sanyuan Zhao, Mang Ye, and Jianbing Shen. Cross-modality person re-identification via modality confusion and center aggregation. In ICCV, pages 16403-16412, 2021.
[13] Kaiming He, Xiangyu Zhang. Shaoging Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.
[14] Shuting He, Hao Luo, Pichao Wang, Fan Wang, Hao Li, and Wei Jiang. TransRelD: Transformer- based object re-identification. In ICCV, pages 15013-15022, 2021.
[15] Zhangyi Hu, Bin Yang, and Mang Ye. Empowering visible-infrared person re-identification with large foundation models. NeurIPS, 37:117333-1173337 2024.
[16] Diederik Kingma and Jimmy Ba. Adam: method for stochastic optimization. ICLR, 2015.
[17] Leijun Li, Qinghua Hu, Xiangqian Wu, and Daren Yu. Exploration of classification confidence ensemble learning. PR, 1709)3120-3111, 2014.
[18] Siyuan Li, Li Sun, and Qingli Li. CLIP-ReID: exploiting vision-language model for image re-identification without concrete text labels. AAAI, volume 37, pages 1405-1413, 2023.
[19] Wenkang Li, Ke Qi, Wenbin Chen, and Yicong Zhou. Unified batch all triplet loss for visible- infrared person re-identification. In IJCNN, pages 1-8. IEEE, 2021.
[20] Zhiyong Li, Haojie Liu, Xiantao Peng, and Wei Jiang. Inter-intra modality knowledge learning and clustering noise alleviation for unsupervised visible-infrared person re-identification. JEEE TKDE, 36(4)33934-3947, 2024.
[21] Wenqi Liang, Guangcong Wang, Jianhuang Lai, and Xiaohua Xie. Homogeneous-to- heterogeneous: Unsupervised learning for rgb-infrared person re-identification. IEEE TIP, 30:6392-6407,
[22] Yan Lu, Yue Wu, Bin Liu, Tianzhu Zhang, Baopu Li, Qi Chu, and Nenghai Yu. Cross-modality person re-identification with shared-specific feature transfer. In CVPR, pages 2020.
[23] Dat Tien Nguyen, Hyung Gil Hong, Ki Wan Kim, and Kang Ryoung Park. Person recognition system based on combination of body images from visible light and thermal cameras. Sensors, 17(3):605, 2017.
11

---

[24] Zhiqi Pang, Chunyu Wang, Lingling Zhao, Yang Liu, and Gaurav Sharma. Cross-modality hierarchical clustering and refinement for unsupervised visible-infrared person re-identification. IEEE TCSVT, 2023.
[25] Zhiqi Pang. Lingling Zhao, Yang Liu, Chunyu Wang, and Gaurav Sharma. Robust labeling and invariance modeling for unsupervised cross-resolution person re-identification. IEEE Trunsuctions on Image Processing, 2025.
[26] Yuxuan Qiu, Liyang Wang, Wei Song, Jiawei Liu, Zhiping Shi, and Na Jiang. Advancing visible-infrared person re-identiiication: Synergizing visual-textual reasoning and cross-modal feature alignment. IEEE TIFS, 2025.
[27] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763, 2021.
[28] Kaijie Ren and Lei Zhang. Implicit discriminative knowledge learning for visible-infrared person re-identification. In CVPR, pages 593-402,22 2024.
[29] Jiangming Shi, Xiangbo Yin, Yeyun Chen, Yachao Zhang, Zhizhong Zhang, Yuan Xie, and Yanyun Qu Multi-memory matching for unsupervised visible-infrared person re-identification. In ECCV, pages 456-474. Springer, 2024.
[30] Jiangming Shi, Xiangbo Yin, Yachao Zhang, Yuan Xie, Yanyun Qu, et. Learning commonality, divergence and variety for unsupervised visible-infrared person re-identification. NeurIPS, 37:99715-99734, 2024.
[31] Ancong Wu, Wei-Shi Zheng, Hong-Xing Yu, Shaogang Gong, and Jianhuang Lai. RGB-infrared mross-modality person re-identification. In ICCV, pages 5380-5389, 2017.
[32] Zesen Wu and Mang Ye. Unsupervised visible-infrared person re-identification via progressive graph matching and alternate learning. CVPR, pages 9548-9558, 2023.
[33] Run-Sen Xia, Xue-Yav Yan Wang, Si-Bao Chen, Jin Tang, and Bin Luo. Camera-proxy enhanced identit-rrcaliibraion learning for unsupervised visible-infrared person re-identification. IEEE TCSVT, 2025.
[34] Kunlun Xu, Chenghao Jiang. Peixi Xiong, Yuxin Peng, and Jiahuan Zhou. Dask: Distribution rehearsing via adaptive style kernel learning for exemplar-free lifelong person re-identification. In AAAI, volume 39, pages 8915-8923, 2025.
[35] Kunlun Xu, Zichen Liu, Xu Zou, Yuxin Peng, and Jiahuan Zhou. Long short-term knowledge decomposition and consolidation for lifelong person re- re-identification. TPAMI, 2025.
[36] Bin Yang. Jun Chen, and Mang Ye. Towards grand unified representation learning for unsuper- vised visible-infrared person re-identification. In ICCV, pages 1069--1077,, 2023.
[37] Bin Yang, Jun Chen, and Mang Ye. Shallow-deep collaborative learning for unsupervised visible-infrared person re-identification. In CVPR, pages 16870-16879, 2024.
[38] Bin Yang, Mang Ye, Jun Chen, and Zesen Wu. Augmented dual-contrastive aggregation learning for unsupervised visible-infrared person re-identification. In ACM MM, pages 2843-2851, 2022.
[39] Mouxing Yang. Zhenyu Huang, Peng Hu, Taihao Li, Jiancheng Lv, and Xi Peng. Learning with twin noisy labels for visible-infrared person re-identification. In CVPR, pages 14308-14317, 2022.
[40] Yiming Yang, Weipeng Hu, and Haifeng Hu. Progressive cross- modal association learning for unsupervised visible-infrared person re-identification. IEEE TIFS, 2025.
[41] Yongquan Yang, Haijun Lv, and Ning Chen. A survey ensemble learning under the era of deep learning. Artificial Intelligence Review, 56(6):5545-5589, 2023.
[42] Mang Ye, Xiangyuan Lan, and Qingming Leng. Modality-aware collaborative learning for visible thermal person re-identification. In ACM MM, pages 347-355, 2019.
12

---

[43] Mang Ye, Weijian Ruan, Bo Du, and Mike Zheng Shou. Channel augmented joint learning for visible-infrared recognition. In ICCV, pages 13567-13576, 2021.
[44] Mang Ye, Jianbing Shen, David Crandall, Ling Shao, and Jiebo Luo. Dynamic dual-attentive aggregation learning for visible-infrared person re-identification. In ECCV, pages 229-247. Springer, 2020.
[45] Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven CH Hoi. Deep learning for person re-identification: A survey and outlook. IEEE TPAMI, 44(6)22722-8893, 2021.
[46] Mang Ye, Zheng Wang. Xiangyuan Lan, and Pong C Yuen. Visible thermal person re-
identification via dual-constrained top-ranking, In IJCAI, volume 1, page 2, 2018.
[47] Mang Ye, Zesen Wu, Cuiqun Chen, and Bo Du. Channel augmentation for visible-infrared re-identificatiin. IEEE TPAMI, 2023.
[48] Mang Ye, Zesen Wu, and Bo Du. Dual-level matching with outlier filtering for unsupervised visible-infrared person re-identification. IEEE TPAMI, 2025.
[49] Hao Yu, Xu Cheng. Wei Peng, Weihao Liu, and Guoying Zhao. Modality unifying network for visible-infrared person re-identification. In ICCV, pages 11 11185-11195, 2023.
[50] Xiaoyan Yu, Neng Dong, Lichuang Zhu, Hao Peng, and Dapeng Tao. Clip-driven semantic discovery network for visible-infrared person re-identification. JEEE TMM, 2025.
[51] Qiang Zhang, Changzhou Lai. Jianan Liu, Nianchang Huang, and Jungong Han. FMCNet: Feature-level modality compensation for visible-infrared person re-identification. In CVPR, pages 7349-7358, 2022.
[52] Yukang Zhang and Hanzi Wang. Diverse embedding expansion network and low-light cross- modality benchmark for visible-infrare person re-identification. In CVPR, pages 2153-2162, 2023.
[53] Yukang Zhang, Yan Yan, Jie Li, and Hanzi Wang. MRCN: A novel modality restitution and compensation network for visible-infrared person re-identification. In AAAI, volume 37, pages 3498-3506, 2023.
[54] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: benchmark. In ICCV, pages 1116-1124, 2015.
[55] Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. In AAAI, volume 34, pages 13001-13008, 2020.
[56] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. IJCV, 13009)2337-2348, 2022.
[57] Haidong Zhu, Pranav Budhwant, Zhaoheng Zheng, and Ram Nevatia. Seas: Shape-aligned supervision for person re-identification. In CVPR, pages 164-174, 2024.
13

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract provides a concise summary the paper's scope and contributions. The introduction first presents detailed overview of the problem background and the scope the paper, and then elaborates on the proposed method and the specific contributions made by this paper.
Justification:
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The final paragraph of the "Conclusion and Limitations" section discusses the current limitations of this work.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "LLimiations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged
the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
14

---

Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
Justification:
The full of theoretical assumptions and corresponding formulations are provided in Section 3. Furthermore, Section 4 presents a detailed analysis validating the correctness and effectiveness of these theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Ys]]
To enhance the reproducibility of our experimental results, we provide detailed description of our method in Section 3. Additionally, key experimental details are presented in the "Implementation Details" subsection to facilitate replication, and the al algorithmic procedure outlined in Supplementary Material Section S.III.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how replicate the results, access to a hosted model (e.g., g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily the architecture clearly and fully.
model architecture, the paper should describe
(c) the contribution a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
15

---

(d)
reproducible, they We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [No]
Justification: Given that our method represents a novel and preliminary attempt, considering releasing a more polished and comprehensive version of the code in the future. In the meantime, we have provided key experimental details in the "Implementation Details" subsection and included an algorithmic procedure in Section S.III of the Supplementary Material to enable researchers to partially or fully reproduce our method.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips... cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https
/
Stte://ggguieeeeeSSuuuuiiiiii ssi ssionPolicy for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data,
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Question:
Answer: [Yes]
Justification:
We provide all relevant training and testing details in Sections 4.1 and 4.2.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [No]
16

---

Justification: We do not report error bars in our results. However, following established practice in prior work. we conduct each experiment 10 times with different random selections splits the SYSU-MM01 and RegDB datasets, and report the average performance over these runs to ensure the stability and reliability of the results.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (c.g., Normally distributed errors).
should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report I-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (c.g. negative error rates).
If error bars reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
We provide sufficient information in Section 4.2.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't 't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics
Answer: [Yes]
Justification:
The research fully conforms in every respect to the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- cration due to laws or regulations their jurisdiction).
10. Broader impacts
17

---

Question:
Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: A discussion of broader impacts is provided in Supplementary Material Section S.V.
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation. generating fake profiles, surveillance), faimess considerations (e.g.. deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
The pretrained models and large lision-language models used in this paper are publicly available and widely adopted in the research community. We do not release any additional pretrained models, and no new models or datasets with potential misuse risks introduced.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (c.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
18

---

Justification: All datasets and models used in this paper have been properly credited, with appropriate citations provided. Their licenses and terms of use have been explicitly ack acknowledged and fully respected.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version the asset used and, possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g.. website), the copyright and terms of service of that source should be provided.
If assets released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, www.ssppppssaaaeea... thcode com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
this information is not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification:
This paper does not introduce any new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details the dataset/code/mmdel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: This paper does not involve crowdsoureing experiments or research with human subjects.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurIPS Code of Ethics, workers involved data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
Institutional review board (IRB) approvals or equivalent for research with human subjects
19

---

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: This paper does not involve crowdsourcing or research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with
human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, or originality of the research, declaration is not required.
Answer: [Ye]]
Justification: We employ large vision-language model (LVLM) as key component to enhance existing methods. The details of its usage are described in Section 3. and further elaborated in the Supplementary Material Section S.I.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non standtandard components.
Please refer to our LLM policy pobtts://neurr: cc/Conferences// /LLM) for what should or should not be described.
20


Reviews:
[{"review_id": "PfDLNVv2eE", "reviewer": "Reviewer_nERK", "summary": "This paper proposes LVLM-AAM, a novel Large Vision-Language Model (LVLM)-driven Attribute-Aware Modeling approach for unsupervised visible-infrared person re-identification (UVI-ReID). The core idea is to leverage attribute arrays extracted by an LVLM to enhance the quality of cluster-based pseudo-labels and guide modality-invariant feature learning. Extensive experiments demonstrate significant performance improvements over existing unsupervised methods and even some supervised baselines on standard VI-ReID datasets.", "questions": "Please kindly refer to the weakness.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. This work explores the highly novel direction by utilizing attribute arrays extracted by LVLM to improve the quality of cluster-based pseudo-labels in UVI-ReID, and designs an explicit-implicit attribute fusion module and an attribute-aware contrastive learning module based on attribute-aware strategies.\n2. The proposed LVLM-AAM method significantly enhances the performance of UVI-ReID models. Extensive experiments are conducted to validate the effectiveness. \n\nWeakness:\n1. The explanation of the attribute-aware reliable labeling strategy in Section 3.2 requires more detail for full clarity. Specifically, it is unclear why attribute-aware matching selects only the first three attributes from the array, while attribute-aware refinement marks samples as outliers based on deviations exceeding η for all attributes. Does this imply different sensitivities or roles for attributes in these two sub-tasks? \n2. The quality and reliability of the attributes extracted by the LVLM are crucial to the method's success, yet insufficiently analyzed. How effective is the LVLM at extracting meaningful attributes specifically from infrared images, given that LVLMs are typically pre-trained predominantly on visible spectrum data? Is there consistency in the attributes extracted for the same person across the visible and infrared modalities? \n3. LVLM inference is computationally intensive. Integrating LVLM attribute extraction for every image and cluster within the ReID pipeline likely incurs significant time and resource overhead.\n4. The relationship and training sequence between the LVLM-derived explicit attributes and the CLIP-learned implicit attributes need clarification: are the implicit attributes (learned via CLIP) acquired before applying the LVLM-AAM framework, as in previous similar works, or are they trained simultaneously?\n5. The formulation of Equation (1) in Section 3.3 appears potentially incorrect due to the lack of normalization by the number of samples in the cluster.", "was_revised": true, "final_justification": "The authors' response has clarified my questions regarding the use of attribute information in the methodology and the model's computational overhead. Employing LVLM to enhance the performance of the VI-ReID task is an innovative approach and merits acceptance.", "rebuttal": "We thank the reviewer for the constructive and detailed feedback.\n\n---\n\n> *Weakness1 and Question1: The explanation of the attribute-aware reliable labeling strategy in Section 3.2 requires more detail for full clarity. Specifically, it is unclear why attribute-aware matching selects only the first three attributes from the array, while attribute-aware refinement marks samples as outliers based on deviations exceeding η for all attributes. Does this imply different sensitivities or roles for attributes in these two sub-tasks?*\n\n**Response:**\nThank you very much for your insightful comments. We use all attributes in the attribute-aware refinement module for outlier detection to align with a realistic perceptual principle: evaluating an object from more dimensions (attributes) is generally more accurate and comprehensive than doing so from fewer dimensions. Therefore, the attribute-aware refinement leverages all available attributes.\n\nFor attribute-aware matching, our initial intention is also to use all available attributes to guide the grouping of clusters. However, we find this impractical in real-world scenarios. This is because clothing descriptions are highly diverse, and LVLM exhibits a certain degree of instability, which leads to varied interpretations of attributes such as “Upper” and “Lower.” For example, the same “Upper” item may be recognized by LVLM as either a “Shirt” or a “Sweatshirt,” resulting in broad value ranges for both “Upper” and “Lower.” If all five attributes are used for cluster grouping in attribute-aware matching, the resulting partitioning becomes overly fine-grained, often leading to groups that contain only visible clusters or only infrared clusters. This negatively impacts the inter-modality matching process. In contrast, the first three attributes in the attribute array—“Gender: [male/female],” “Glasses: [wearing/without],” and “Backpack: [carrying/without],”—have only two possible values each. By grouping clusters based on these three attributes, we are able to effectively leverage the knowledge provided by the LVLM while ensuring that each group contains both visible and infrared clusters. Therefore, as stated in lines 163-165 of the manuscript, “we choose the first three attributes because each has a countable value space, which ensures that each group contains both visible and infrared clusters.”\n\nMoreover, since the attribute-aware refinement only considers images with deviations exceeding $\\eta$ (where $\\eta = 2$) as outliers, it demonstrates a certain degree of tolerance and robustness to the variability or inaccuracy in the values of attributes such as “Upper” and “Lower.” As a result, it is feasible to utilize all five attributes in the attribute-aware refinement process.\n\nTherefore, as you correctly point out, the attributes indeed play different roles in the two modules, and attribute-aware matching is generally more sensitive to attribute variability than attribute-aware refinement. We will follow your suggestion to revise Section 3.2 to improve its clarity.\n\n---\n\n> *Weakness2 and Question2: The quality and reliability of the attributes extracted by the LVLM are crucial to the method's success, yet insufficiently analyzed. How effective is the LVLM at extracting meaningful attributes specifically from infrared images, given that LVLMs are typically pre-trained predominantly on visible spectrum data? Is there consistency in the attributes extracted for the same person across the visible and infrared modalities?*\n\n**Response:**\nThank you for your question. We take three main measures to ensure that the attributes extracted by the LVLM are effective for the ReID task. First, as shown in the supplementary material (Section S.I), we explicitly include the instruction “Exclude all color descriptions” when prompting the LVLM, to guide it to ignore color differences between visible and infrared images. Second, we evaluate several versions of the LVLMs, including Qwen2.5-VL-3B-Instruct, Qwen2.5-VL-7B-Instruct, Qwen2.5-VL-32B-Instruct, and Qwen2.5-VL-72B-Instruct. Balancing performance and computational cost, we choose Qwen2.5-VL-32B-Instruct, which provides a good trade-off, to extract image attributes and ensure their reliability. Third, in the attribute-aware matching module, we incorporate only the attributes “Gender,” “Glasses,” and “Backpack” during the inter-modality matching stage. These three attributes are typically invariant to the style shift caused by the infrared modality. Moreover, we define a compact value space for them—“Gender: [male/female],” “Glasses: [wearing/without],” and “Backpack: [carrying/without]”—to encourage consistency of these attributes across modalities. \n\nBased on the above three efforts, we observe that the inconsistency rate of the first three attributes for the same person across the visible and infrared modalities is less than 7%. \n\n---\n\n> *Weakness3 and Question3: LVLM inference is computationally intensive. Integrating LVLM attribute extraction for every image and cluster within the ReID pipeline likely incurs significant time and resource overhead.*\n\n**Response:**\nThank you very much for your comment. We acknowledge that LVLM inference is computationally intensive. For our task, the Qwen2.5-VL-32B-Instruct model is sufficient to improve the performance of our ReID framework without incurring prohibitive costs. Specifically, Qwen2.5-VL-32B-Instruct runs efficiently on our ReID training hardware (i.e., four NVIDIA GeForce RTX 4090 GPUs), and the inference time is generally acceptable. For example, extracting image-level attributes for all training images in the SYSU-MM01 dataset typically takes around 15 hours. Moreover, obtaining cluster-level attribute arrays does not involve additional LVLM inference. Instead, it only requires computing the mode of all image-level attribute arrays within a cluster, which is a very fast operation. This process usually takes only a few seconds for the entire dataset.\n\nIn addition, the LVLM inference is only required once during the training phase to extract identity attributes and is not needed during the testing phase. Therefore, the computational cost associated with LVLM inference does not affect the inference speed of the trained ReID model during testing.\n\n---\n\n> *Weakness4 and Question4: The relationship and training sequence between the LVLM-derived explicit attributes and the CLIP-learned implicit attributes need clarification: are the implicit attributes (learned via CLIP) acquired before applying the LVLM-AAM framework, as in previous similar works, or are they trained simultaneously?*\n\n**Response:**\nThank you for your question. As described in lines 195-197 of the manuscript, we first combine the LVLM-derived explicit attributes with the implicit attributes by assigning to each cluster a text prompt containing learnable text embeddings “$\\left[ {{X_1}} \\right]{\\rm{ }}\\left[ {{X_2}} \\right] \\ldots \\ldots \\left[ {{X_M}} \\right]$” in the format:\n“An image of a $\\left[ {{X_1}} \\right]{\\rm{ }}\\left[ {{X_2}} \\right] \\ldots \\ldots \\left[ {{X_M}} \\right]$ $[male/female]$ $[wearing/without]$ glasses and $[carrying/without]$ a backpack.” Subsequently, we introduce the CLIP contrastive loss (as defined in Equation (6)) to optimize the learnable text embeddings.\n\nTherefore, the explicit and implicit attributes are jointly fed into the model. In addition, only the implicit attributes (i.e., the learnable text embeddings) are optimized, while the explicit attributes remain fixed throughout training.\n\n---\n\n> *Weakness5 and Question5: The formulation of Equation (1) in Section 3.3 appears potentially incorrect due to the lack of normalization by the number of samples in the cluster.*\n\n**Response:**\nWe apologize for the confusion and would like to provide further clarification regarding Equation (1). Since the number of samples within each cluster may vary, we use $N_p^v$ to denote the number of images (samples) in the $p$-th cluster. The subscript of $N_p^v$ corresponds directly to the cluster index $p$. Therefore, in Equation (1), each cluster is associated with its own intra-cluster sample count."}, {"review_id": "NJP8HLZ6mm", "reviewer": "Reviewer_pR1y", "summary": "This paper presents LVLM-AAM, an attribute-aware modeling method for unsupervised visible-infrared person re-identification. By leveraging a large vision-language model (LVLM) to extract attributes like gender and clothing, the method refines pseudo-labels and enhances modality-invariant learning. It features attribute-aware reliable labeling, attribute fusion, and contrastive learning, outperforming existing unsupervised and some supervised methods on SYSU-MM01 and RegDB datasets.", "questions": "1. The paper should analyze the effectiveness of EAF and AAC in a fully supervised setting.\n2. It lacks an analysis of hyper-parameters such as alpha, lambda, and eta.\n3. The title should include \"Unsupervised\" unless the paper aims to demonstrate the method's effectiveness in both unsupervised and supervised Visible-Infrared Person Re-Identification scenarios, in which case experiments in the supervised context should be added.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths :\nThe experimental results on SYSU-MM01 and RegDB datasets achieve state-of-the-art performance.\n\nWeaknesses:\nThe attributes generated by LVLM are identical to those created manually, differing only in efficiency. Consequently, the AR and AM modules are straightforward and not novel, with no direct connection to LVLM. The use of attributes itself is not innovative. Therefore, the main contribution remains in the cross-modality alignment components, specifically EAF and AAC, which require further analysis to validate their effectiveness.", "was_revised": true, "final_justification": "I appreciate the detailed response, which has resolved my questions. I am satisfied and will accept it.", "rebuttal": "We thank the reviewer for the constructive and detailed feedback.\n\n---\n\n> *Weaknesses: The attributes generated by LVLM are identical to those created manually, differing only in efficiency. Consequently, the AR and AM modules are straightforward and not novel, with no direct connection to LVLM. The use of attributes itself is not innovative. Therefore, the main contribution remains in the cross-modality alignment components, specifically EAF and AAC, which require further analysis to validate their effectiveness.*\n\n**Response:**\nThank you for your comment. As you point out, using LVLM-generated attributes to replace manually annotated ones indeed improves efficiency, which is a key factor in promoting the practical deployment of ReID systems.\n\nThe core contribution and novelty of this paper lie in leveraging supervision information (i.e., attribute arrays) generated by an LVLM to improve the performance of unsupervised visible-infrared person re-identification. This represents a novel research direction that, to the best of our knowledge, has not been explored in existing visible-infrared person re-identification methods. The other three reviewers also describe this as a “highly novel direction.” Building upon this direction, we design four key modules: attribute-aware refinement (AR), attribute-aware matching (AM), explicit-implicit attribute fusion (EAF), and attribute-aware contrastive learning (AAC). Among them, AR and AM utilize LVLM-derived attribute arrays to effectively enhance the reliability of pseudo-labels and improve model performance. These components are absent from prior methods and demonstrate how the powerful capabilities of LVLMs can be effectively connected to and leveraged within the ReID task.\n\nFollowing your suggestion, we evaluate the effectiveness of EAF and AAC under the fully supervised setting. Relevant details are provided in the response below.\n\n---\n\n> *Question1: The paper should analyze the effectiveness of EAF and AAC in a fully supervised setting.*\n\n**Response:**\nThank you very much for your guidance. We have evaluated the effectiveness of EAF and AAC under the fully supervised setting. In this setting, the baseline method uses ground-truth identity labels instead of the pseudo-labels generated by the proposed attribute-aware reliable labeling strategy. The detailed setup follows CLIP-VIReID+MsPL [1]. We then incorporate EAF and AAC into the baseline method individually to assess their contributions. \n\nThe ablation analysis of EAF and AAC under fully supervised settings is shown in the Table below, where we report the Rank-1 accuracy for each variant. EAF effectively improves model performance by embedding attribute arrays from the LVLM into text prompts. Specifically, Baseline+EAF improves Rank-1 accuracy over the Baseline by 3.38% and 5.27% on SYSU-MM01 (All Search and Indoor Search, respectively), and by 7.04% on RegDB (Visible to Thermal). Furthermore, AAC introduces dynamic text features to further enhance performance. Compared to Baseline+EAF, Baseline+EAF+AAC achieves additional Rank-1 improvements of 1.05% and 1.12% on SYSU-MM01 (All Search and Indoor Search, respectively), and 0.93% on RegDB (Visible to Thermal). Similarly, Baseline+AAC also outperforms the Baseline. These results validate the effectiveness of both EAF and AAC under the fully supervised setting.\n\n|      Methods       | SYSU-MM01 (All Search) | SYSU-MM01 (Indoor Search) | RegDB (Visible to Thermal) |\n|------------------|:----------------------:|:--------------------------:|:---------------------------:|\n|     Baseline       |         72.89          |           78.67           |            87.60           |\n|   Baseline+EAF     |         76.27          |           83.94           |            94.64           |\n|   Baseline+AAC     |         75.63          |           81.79           |            91.53           |\n| Baseline+EAF+AAC   |         77.32          |           85.06           |            95.57           |\n\n---\n\n> *Question2: It lacks an analysis of hyper-parameters such as alpha, lambda, and eta.*\n\n**Response:**\nThank you for your comment. We set $\\alpha = 0.50$ to ensure fairness between modalities. Specifically, in the dynamic text feature, the information from the infrared and visible modalities should contribute equally to maintain a certain degree of modality invariance. In addition, we also experiment with $\\alpha = 0.25$ and $\\alpha = 0.75$, and find that the model achieves better performance when $\\alpha = 0.50$ compared to the other two settings. This result supports our design choice.\n\nWe set $\\lambda_{inter} = 0.50$ by following the configuration used in existing methods [2]. In addition, we experiment with a range of values including [0.00, 0.25, 0.50, 0.75, 1.00], and observe that the model achieves the best performance when $\\lambda_{inter} = 0.50$. This may be attributed to the fact that inter-modality pseudo-labels are generally less reliable than intra-modality pseudo-labels. Therefore, while $L_{inter}$ contributes to performance improvement, its weight $\\lambda_{inter}$ should be significantly lower than the weight of $L_{intra}$, which is set to 1.\n\nFor $\\lambda_{tsc}$ and $\\eta$, we provide experimental results and analysis in Section S.IV of the supplementary material.\n\n---\n\n> *Question3: The title should include \"Unsupervised\" unless the paper aims to demonstrate the method's effectiveness in both unsupervised and supervised Visible-Infrared Person Re-Identification scenarios, in which case experiments in the supervised context should be added.*\n\n**Response:**\nThank you for your insightful comment. We did not include the term “Unsupervised” in the title because LVLM-AAM leverages supervision information (i.e., attribute arrays) extracted from an LVLM, and thus may not be strictly categorized as a fully unsupervised method. As stated in Lines 62-66 of the manuscript: “since LVLM-AAM leverages supervision information (i.e., attribute arrays) extracted from an LVLM, it may not be considered a fully UVI-ReID method. In other words, the primary goal of this work is to explore the effectiveness of utilizing an LVLM to advance the practical application of UVI-ReID -- specifically, to improve recognition performance while maintaining low manual annotation costs.”\n\nFollowing your suggestion, we evaluate the proposed method in the supervised visible-infrared person re-identification scenario and compare its performance with existing supervised methods. Specifically, under the fully supervised setting, we replace the pseudo-labels generated by the proposed attribute-aware reliable labeling with the ground-truth identity labels. The comparison between the proposed LVLM-AAM and existing state-of-the-art supervised methods on the SYSU-MM01 and RegDB datasets is presented in the Table below, where we report the Rank-1 accuracy for each method. Although the proposed LVLM-AAM is not specifically designed for the supervised scenario, it still achieves competitive performance under the supervised setting. For example, LVLM-AAM performs comparably to state-of-the-art supervised methods such as SAAI [3] and CSDN [1] on SYSU-MM01, and even outperforms existing supervised methods on RegDB. This is attributed to the ability of LVLM-AAM to effectively leverage the attribute array from the LVLM to assist model optimization. These results validate the effectiveness of the proposed LVLM-AAM in the supervised scenario.\n\n|     Methods     | SYSU-MM01 (All Search) | SYSU-MM01 (Indoor Search) | RegDB (Visible to Thermal) |\n|---------------|:----------------------:|:--------------------------:|:---------------------------:|\n|   SAAI [3]      |         75.90          |           83.20           |            91.07           |\n|   IDKL [4]      |         81.42          |           87.14           |            94.72           |\n| STAR-ReID [5]   |         82.93          |           88.04           |            91.89           |\n|   CSDN [1]      |         76.70          |           84.50           |            95.40           |\n|   LVLM-AAM      |         77.32          |           85.06           |            95.57           |\n\nWe will include the above experiments and analysis in the revised manuscript and supplementary materials.\n\n---\n\n[1] Xiaoyan Yu, Neng Dong, Liehuang Zhu, Hao Peng, and Dapeng Tao. Clip-driven semantic discovery network for visible-infrared person re-identification. IEEE Transactions on Multimedia, 2025.\n\n[2] Zesen Wu and Mang Ye. Unsupervised visible-infrared person re-identification via progressive graph matching and alternate learning. In CVPR, pages 9548-9558, 2023.\n\n[3] Xingye Fang, Yang Yang, and Ying Fu. Visible-infrared person re-identification via semantic alignment and affinity inference. In ICCV, pages 11270-11279, 2023.\n\n[4] Kaijie Ren and Lei Zhang. Implicit discriminative knowledge learning for visible-infrared person re-identification. In CVPR, pages 393-402, 2024.\n\n[5] Yuxuan Qiu, Liyang Wang, Wei Song, Jiawei Liu, Zhiping Shi, and Na Jiang. Advancing visible-infrared person re-identification: Synergizing visual-textual reasoning and cross-modal feature alignment. IEEE TIFS, 2025."}, {"review_id": "9OTeVLnXjk", "reviewer": "Reviewer_B8pp", "summary": "This paper proposes an approach to enhance unsupervised visible-infrared person re-identification by leveraging large vision-language models (LVLMs). Specifically, the authors utilize attribute arrays extracted from an LVLM to refine both intra-modality clustering and inter-modality matching. Furthermore, attribute information is embedded into the text to enrich semantic guidance. The method also incorporates both dynamic and static text features to facilitate modality-invariant feature learning. Experimental results on two datasets demonstrate the superiority of the proposed method, outperforming existing unsupervised methods and even some supervised methods. The effectiveness of each component is further supported by comprehensive ablation studies.", "questions": "1. Does the proposed method demonstrate generalizability? Specifically, have the authors evaluated the proposed method on broader datasets such as LLCM [1] to assess its robustness and effectiveness?\n2. What is the underlying rationale for the joint use of dynamic and static text features in promoting modality-invariant feature learning? A more detailed explanation or theoretical justification would enhance the clarity of this design choice.\n3. During the inter-modality contrastive learning stage, the proposed method appears to rely on previously saved pseudo-labels rather than generating new ones through clustering algorithms. This seems to diverge from several existing methods [2, 3] that dynamically update pseudo-labels in each epoch. Could the authors clarify the motivation behind this decision?\n\nIf the authors are able to provide well-reasoned responses to the above concerns, I would be willing to raise my rating.\n\n[1] Yukang Zhang, Hanzi Wang. Diverse embedding expansion network and low-light cross-modality benchmark for visible-infrared person re-identification. In CVPR, 2153-2162, 2023.\n[2] Bin Yang, Mang Ye, Jun Chen, Zesen Wu. Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification. In ACM MM, 2843–2851, 2022.\n[3] Zesen Wu, Mang Ye. Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning. In CVPR, 9548-9558, 2023.", "limitations": "Yes", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. This work appears to be one of the early attempts to explore the use of Large Vision-Language Models (LVLMs) in the context of unsupervised visible-infrared person re-identification. The proposed method effectively leverages attribute arrays generated by an LVLM to enhance performance, making the study both meaningful and novel.\n2. The experimental results are convincing. In particular, Figures 3 and 4 in the ablation study provide strong evidence supporting the effectiveness of the key components introduced by the authors.\n3. The manuscript is generally well written, with a clear presentation and a logical organization of sections. The inclusion of the Algorithmic Procedure helps elucidate the proposed method.\nWeaknesses:\n1. The generalizability of the proposed method is not sufficiently demonstrated, as experiments are limited to only two datasets, with RegDB being relatively small in scale.\n2. The authors claim that static text features are modality-dependent. However, they do not provide a clear explanation as to why combining dynamic and static text features facilitates the modality-invariant feature learning.", "was_revised": true, "final_justification": "The validation regarding generalizability, as well as the explanations on modality-invariant feature learning and pseudo-labels, are effective and satisfactory. I consider this to be an interesting paper with solid contributions, and I have accordingly raised my rating to Strong Accept.", "rebuttal": "We thank the reviewer for the constructive and detailed feedback.\n\n---\n\n> *Weakness1 and Question1: Does the proposed method demonstrate generalizability? Specifically, have the authors evaluated the proposed method on broader datasets such as LLCM to assess its robustness and effectiveness?*\n\n**Response:**\nThank you for your valuable comments. To evaluate the generalization ability of the proposed LVLM-AAM, we assess its performance on the LLCM dataset. The comparison between the proposed LVLM-AAM and existing state-of-the-art unsupervised methods on the LLCM dataset is presented in the Table below, where we report the Rank-1 accuracy for each method. Our method outperforms existing methods in both testing scenarios of LLCM. For instance, compared to SDCL [3], LVLM-AAM achieves a more significant performance gain on LLCM than on SYSU-MM01. Moreover, despite SCA-RCP [4] utilizing camera labels, LVLM-AAM still demonstrates a substantial advantage. This is because LLCM presents greater complexity compared to SYSU-MM01 and RegDB, making it more challenging for existing methods to obtain reliable pseudo-labels. In contrast, LVLM-AAM effectively leverages attribute arrays from the LVLM to enhance the reliability of pseudo-labels and utilizes text semantics to facilitate model optimization, thereby achieving superior performance over existing methods. These experimental results not only validate the superiority of the proposed LVLM-AAM but also demonstrate its strong generalization capability.\n\n|    Methods    | LLCM (Visible to Infrared) | LLCM (Infrared to Visible) |\n|-------------|:--------------------------:|:---------------------------:|\n|  CCLNet [1]   |           45.3             |            39.3             |\n|   PGM [2]     |           44.4             |            38.4             |\n|  SDCL [3]     |           46.9             |            43.4             |\n| SCA-RCP [4]   |           29.1             |            22.3             |\n|  LVLM-AAM     |           52.2             |            46.0             |\n\n---\n\n> *Weakness2 and Question2: What is the underlying rationale for the joint use of dynamic and static text features in promoting modality-invariant feature learning? A more detailed explanation or theoretical justification would enhance the clarity of this design choice.*\n\n**Response:**\nSince the static text features are learned separately within each modality, they typically contain modality-specific information. As shown in Equation (9) (with $\\alpha = 0.5$), the dynamic text feature incorporates equal contributions from both the visible and infrared modalities, and thus tends to be more modality-invariant compared to the static text features. Therefore, in Equation (10), by encouraging image features to align with the dynamic text feature while being pushed away from the static text features of the same modality, our method has the potential to promote modality-invariant feature learning.\n\nMoreover, since the dynamic text feature is derived from two static text features that share the same inter-modality pseudo-label across the visible and infrared modalities, it not only possesses modality-invariant information but also retains identity-related information. Therefore, incorporating the dynamic text feature has the potential to facilitate both modality-invariant feature learning and identity-discriminative feature learning.\n\n---\n\n> *Question3: During the inter-modality contrastive learning stage, the proposed method appears to rely on previously saved pseudo-labels rather than generating new ones through clustering algorithms. This seems to diverge from several existing methods that dynamically update pseudo-labels in each epoch. Could the authors clarify the motivation behind this decision?*\n\n**Response:**\nThank you for your critical question. Our inter-modality contrastive learning indeed relies on previously saved pseudo-labels rather than generating new ones through clustering algorithms. This design choice is based on two main considerations. First, we observe in our experiments that the pseudo-labels generated by clustering during the inter-modality contrastive learning stage are largely consistent with those obtained in the intra-modality contrastive learning stage, with only a small portion differing. Therefore, using the saved pseudo-labels allows us to maintain pseudo-label accuracy while significantly reducing the computational cost during training. Second, in the explicit-implicit attribute fusion stage, we optimize the learnable text embeddings based on the pseudo-labels saved from the intra-modality contrastive learning stage and use the resulting text features to guide the optimization of the image encoder. To ensure consistency between the supervision signals provided by the text features and the pseudo-labels, we choose not to regenerate pseudo-labels during the inter-modality contrastive learning stage. This strategy helps maintain training stability while also improving efficiency.\n\nWe will include the above experiments and analysis in the revised manuscript and supplementary materials.\n\n---\n\n[1] Zhong Chen, Zhizhong Zhang, Xin Tan, Yanyun Qu, and Yuan Xie. Unveiling the power of clip in unsupervised visible-infrared person re-identification. In ACM MM, pages 3667-3675, 2023.\n\n[2] Zesen Wu and Mang Ye. Unsupervised visible-infrared person re-identification via progressive graph matching and alternate learning. In CVPR, pages 9548-9558, 2023.\n\n[3] Bin Yang, Jun Chen, and Mang Ye. Shallow-deep collaborative learning for unsupervised visible-infrared person re-identification. In CVPR, pages 16870-16879, 2024.\n\n[4] Zhiyong Li, Haojie Liu, Xiantao Peng, and Wei Jiang. Inter-intra modality knowledge learning and clustering noise alleviation for unsupervised visible-infrared person re-identification. IEEE TKDE, 36(8): 3934-3947, 2024."}, {"review_id": "hgshhSPBYO", "reviewer": "Reviewer_nF4X", "summary": "This paper proposes a novel LVLM-driven Attribute-Aware Modeling (LVLM-AAM) method for unsupervised visible-infrared person re-identification (UVI-ReID). In contrast to existing approaches, the authors leverage attribute arrays extracted from a Large Vision-Language Models (LVLM) not only to refine pseudo-labels but also to incorporate attribute text semantics as supervisory signals for model optimization. Furthermore, both static and dynamic text features are integrated to enhance feature learning. Experimental results on the SYSU-MM01 and RegDB datasets demonstrate that the proposed LVLM-AAM achieves competitive performance and significantly outperforms a range of state-of-the-art methods.", "questions": "1. To what extent might the design of the attribute arrays be biased toward the SYSU-MM01 and RegDB datasets? More specifically, does the methodology involving attribute arrays impose constraints on the generalizability of the approach? The authors’ response to this concern will significantly influence my decision regarding the final evaluation score.\n2. Have the authors measured the computational cost associated with extracting attribute arrays using the LVLM? For instance, it would be helpful to report metrics such as memory consumption and inference time. Is this process computationally expensive?\n3. The performance of the proposed model appears to be closely tied to that of the underlying LVLM. Have the authors experimented with different LVLMs? Does achieving strong performance require considerable effort in selecting a high-performing LVLM?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. This paper introduces a novel perspective by leveraging attribute arrays extracted from an LVLM to enhance UVI-ReID. This approach not only reduces the reliance on manually annotated data but also yields significant performance gains. Given that many existing methods in this field primarily focus on extracting knowledge from smaller pretrained models, the proposed strategy offers meaningful insights for future research.\n2. The methodology is well-motivated and clearly articulated. The attribute-aware reliable labeling strategy effectively mitigates the common issue of low-quality pseudo-labels in unsupervised learning, while the attribute-aware contrastive learning module successfully exploits semantic information to facilitate the modality-invariant feature learning.\n3. The proposed method achieves competitive results, outperforming not only existing unsupervised approaches but also several supervised ones.\n\nWeaknesses:\n1. The most critical concern lies in the potential overfitting of the proposed LVLM-AAM framework to the SYSU-MM01 and RegDB datasets, due to the handcrafted nature of the attribute arrays. If these arrays were tailored with prior knowledge of these benchmarks, the generalizability of the method to other datasets may be limited.\n2. Although incorporating an LVLM has the potential to enhance recognition performance, it inevitably introduces additional computational overhead, which may hinder deployment in resource-constrained scenarios.\n3. The performance of the model appears to be heavily dependent on the quality of the underlying LVLM. Selecting and configuring a high-performing LVLM can be both time-consuming and computationally expensive, which could pose practical challenges for broader adoption.", "was_revised": true, "final_justification": "This paper proposes to utilize attribute arrays extracted from an LVLM to improve Unsupervised Visible-Infrared Person Re-Identification, which is a novel and meaningful attempt to introduce visual-language into the person re-identification scenario. Please incorporate experimental results and discussion into the revised version.", "rebuttal": "We thank the reviewer for the constructive and detailed feedback.\n\n---\n\n> *Weakness1 and Question1: To what extent might the design of the attribute arrays be biased toward the SYSU-MM01 and RegDB datasets? More specifically, does the methodology involving attribute arrays impose constraints on the generalizability of the approach? The authors’ response to this concern will significantly influence my decision regarding the final evaluation score.*\n\n**Response:**\nThank you for your comment. The attribute arrays are not specifically designed for the SYSU-MM01 and RegDB datasets. Instead, they consist of general and commonly observed human attributes. Therefore, the attribute arrays do not limit the generalizability of the proposed method. \n\nTo evaluate the generalization ability of the proposed LVLM-AAM, we assess its performance on the LLCM dataset. The comparison between the proposed LVLM-AAM and existing state-of-the-art unsupervised methods on the LLCM dataset is presented in the Table below, where we report the Rank-1 accuracy for each method. Our method outperforms existing methods in both testing scenarios of LLCM. For instance, compared to SDCL [3], LVLM-AAM achieves a more significant performance gain on LLCM than on SYSU-MM01. Moreover, despite SCA-RCP [4] utilizing camera labels, LVLM-AAM still demonstrates a substantial advantage. This is because LLCM presents greater complexity compared to SYSU-MM01 and RegDB, making it more challenging for existing methods to obtain reliable pseudo-labels. In contrast, LVLM-AAM effectively leverages attribute arrays from the LVLM to enhance the reliability of pseudo-labels and utilizes text semantics to facilitate model optimization, thereby achieving superior performance over existing methods. These experimental results not only validate the superiority of the proposed LVLM-AAM but also demonstrate its strong generalization capability.\n\n|    Methods    | LLCM (Visible to Infrared) | LLCM (Infrared to Visible) |\n|-------------|:--------------------------:|:---------------------------:|\n|  CCLNet [1]   |           45.3             |            39.3             |\n|   PGM [2]     |           44.4             |            38.4             |\n|  SDCL [3]     |           46.9             |            43.4             |\n| SCA-RCP [4]   |           29.1             |            22.3             |\n|  LVLM-AAM     |           52.2             |            46.0             |\n\n---\n\n> *Weakness2 and Question2: Have the authors measured the computational cost associated with extracting attribute arrays using the LVLM? For instance, it would be helpful to report metrics such as memory consumption and inference time. Is this process computationally expensive?*\n\n**Response:**\nIn our experiments, we use Qwen2.5-VL-32B-Instruct, a moderately sized model, to extract attribute arrays. In terms of memory consumption, Qwen2.5-VL-32B-Instruct requires approximately 66 GB of GPU memory, which can be easily accommodated by our hardware setup (i.e., four NVIDIA GeForce RTX 4090 GPUs) used for training the ReID model. The inference time of Qwen2.5-VL-32B-Instruct is also generally acceptable. For example, extracting attributes for all training images in the SYSU-MM01 dataset typically takes around 15 hours.\n\nIn summary, the computational cost associated with the LVLM is not prohibitively high. Moreover, since the LVLM is only used during the training phase to extract attributes, it does not affect the inference speed of the trained ReID model during testing.\n\n---\n\n> *Weakness3 and Question3: The performance of the proposed model appears to be closely tied to that of the underlying LVLM. Have the authors experimented with different LVLMs? Does achieving strong performance require considerable effort in selecting a high-performing LVLM?*\n\n**Response:**\nThank you for your question. The performance of the proposed method is indeed influenced by the capability of the underlying LVLM. Specifically, we experiment with several LVLM models, including Qwen2.5-VL-3B-Instruct, Qwen2.5-VL-7B-Instruct, Qwen2.5-VL-32B-Instruct, and Qwen2.5-VL-72B-Instruct.\n\nIn each trial, we first use the LVLM to extract identity-related attributes, which are then used to guide the training of the ReID model. The entire process associated with Qwen2.5-VL-3B-Instruct and Qwen2.5-VL-7B-Instruct takes less than one day, while the process with Qwen2.5-VL-32B-Instruct and Qwen2.5-VL-72B-Instruct takes less than two days. As a result, selecting a high-performing LVLM can typically be completed within a week, which is often shorter than the overall hyperparameter tuning time of the ReID model. Therefore, identifying a suitable LVLM does not require a substantial amount of effort.\n\nFinally, we select Qwen2.5-VL-32B-Instruct, which offers a good trade-off between computational cost and performance, to extract attributes, ensuring the reliability of the attributes used in our method.\n\nWe will include the above experiments and analysis in the revised manuscript and supplementary materials.\n\n---\n\n[1] Zhong Chen, Zhizhong Zhang, Xin Tan, Yanyun Qu, and Yuan Xie. Unveiling the power of clip in unsupervised visible-infrared person re-identification. In ACM MM, pages 3667-3675, 2023.\n\n[2] Zesen Wu and Mang Ye. Unsupervised visible-infrared person re-identification via progressive graph matching and alternate learning. In CVPR, pages 9548-9558, 2023.\n\n[3] Bin Yang, Jun Chen, and Mang Ye. Shallow-deep collaborative learning for unsupervised visible-infrared person re-identification. In CVPR, pages 16870-16879, 2024.\n\n[4] Zhiyong Li, Haojie Liu, Xiantao Peng, and Wei Jiang. Inter-intra modality knowledge learning and clustering noise alleviation for unsupervised visible-infrared person re-identification. IEEE TKDE, 36(8): 3934-3947, 2024."}]

Author rebuttal:

Meta-review: This paper proposes an attribute-aware modeling method LVLM-AAM for unsupervised visible-infrared person re-identification, which uses a large vision-language model to extract attributes, and enhances modality-invariant learning. Experimental results on two datasets demonstrate the superiority of the proposed method.

This paper constructs an intersting method and achieve good performances, all reviewers give positive scores and their concerns are well addressed during the discussion phase. After complementing the statements and experiments provided in the discussion phase, this paper is expected to reach the acceptance level of NeurIPS 2025.
Decision: Accept (poster)