Miss-ReID: Delivering Robust Multi-Modality Object Re-Identification Despite Missing Modalities
Authors: Ruida Xi
Venue: NeurIPS 2025 poster ()
Keywords: Multi-Modality Object Re-Identification
Missing Modalities
Vision-Language Foundation Models
Primary area: applications

Abstract: Multi-modality object Re-IDentification (ReID) targets to retrieve special objects by integrating complementary information from diverse visual sources.
However, existing models that are trained on modality-complete datasets typically exhibit significantly degraded discrimination
during inference with modality-incomplete inputs.
This disparity highlights the necessity of developing a robust multi-modality ReID model that remains effective in real-world applications. For that, this paper delivers a flexible framework tailored for more realistic multi-modality retrieval scenario, dubbed as Miss-ReID, which is the first work to friendly support both the modality-missing training and inference conditions. The core of Miss-ReID lies in compensating for missing visual cues via vision-text knowledge transfer driven by Vision-Language foundation Models (VLMs), effectively mitigating performance degradation. In brief, we capture diverse visual features from accessible modalities first, and then build memory banks to store heterogeneous prototypes for each identity, preserving multi-modality characteristics. Afterwards, we employ structure-aware query interactions to dynamically distill modality-invariant object structures from existing localized visual patches, which are further reversed into pseudo-word tokens that encapsulate the identity-relevant structural semantics.
In tandem, the inverted tokens, integrated with learnable modality prompts, are embedded into crafted textual template to form the personalized linguistic descriptions tailored for diverse modalities.
Ultimately, harnessing VLMs' inherent vision-text alignment capability, the resulting textual features effectively function as compensatory semantic representations for missing visual modalities, after being optimized with some memory-based alignment constraints.
Extensive experiments demonstrate our model's efficacy and superiority over state-of-the-art methods in various modality-missing scenarios, and our endeavors further propel multi-modality ReID into real-world applications.

Miss-RelD: Delivering Robust Multi-Modality Object Re-Identification Despite Missing Modalities
Ruida Xi
State Key Laboratory of Electromechanical Integrated
Manufacturing of nigh-Peeffrrance Electronic Equipment, Xidian University ruidaxi @stu cetaaaiistuiiiidaann xidian. edu.. cn
Abstract
Multi-modality object Re-IDentification (ReID) targets to retrieve special objects by integrating complementary information from diverse visual sources. However, existing models that are trained on modality-complet datasets typically exhibit significantly degraded discrimination during inference with podallty-incomplett inputs. This disparity highlights the necessity of developing robust multi- multi-modality ReID model that remains effective real-world applications. For that, this paper delivers a flexible framework tailored for more realistic multi-modalit retrieval scenario, dubbed as Miss-ReID, which is the first work to friendly support both the modality-missiine training and inference conditions. The core of Miss-RelD lies in compensating for missing visual cues via vision-text knowledge transfer driven by Vision-Languaae foundation Models (VLMs),, effectively mitigating per- formance degradation. In brief, we capture diverse visual features from accessible modalities first, and then build memory banks to store heterogeneous prototypes for each identity, preserving multi-modality characteristics. Afterwards, we employ structure-ane aware query interactions to dynamically distill modality- invariant object structures from existing localized visual patches, which are further reversed into pseudo-word tokens that encapsulate the identity-relevant structural semantics. tandem, the inverted tokens, integrated with learnable modality prompts, are em- bedded into crafted textual template to form the personalized linguistic descriptions tailored for diverse modalities. Ultimately, harnessing VI.Ms' inherent vision-text alignment capability, the resulting textual features effectively function as compen- satory semantic representations for missing visual modalities. after being optimized with some memory-basd alignment constraints. Extensive experiments demon- strate our model's efficacy and superiority over state-of-the- methods in various modality-missing scenarios, and our endeavors further propel multi- modality RelD into real-world applications.
Introduction
Object Re-Identification (ReID) aims retrieve specific objects, such pedestrians, vehicles and other trackable entities, across non-overlapping cameras. Despite remarkable progress in traditional RGB-based single-modality object RelD over recent decades [1-7], its robustness remains compro- mised in complex environments, such as low light and varying weather. Fortunately, multi-modal imaging technologies emerge as a promising solution, effectively mitigating the limitations of single- modality ReID by integrating complementary information from diverse visual sources such as RGB, Near Infrared (NIR) and Thermal Infrared (TIR) modalities. Consequently, multi-modality object ReID methods have garnered significant attention in this filed [8-15].
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Inferencee
Training Inference
Object Structure Modeling
RGB
NIR
TIR
RGB
NIR
TIR
Modallty-imvarian
RNT
Structure
RGB
Mi
TIR
NIR
TIR
Imageente
Modality Complete
Rank-1
(N 3)
Ideenity Proudo
RGANT207
RNT
Text Templete
RGB Modality Prompt
Vision Langusge
Foundittion Model
ENT
Modality Complete (W
Modnllty
Semarnie-aigred RGB Textual Festure
TOP-dwil
REST
(MAndeityymmsssgn Training)
o Dury
Outs
Mocslity Misaing
(Moddaliy complette /Madaing//Mddallyyy complete Training!
(a) Existing Models
(b) Ours
Performance Comparisons
Illustrative comparisons between existing multi-modality object ReID methods and ours. (a) Prior works, when trained on during inference under modality-complee conditions, while showing degraded performances when encountered with modality-missin
Figure 1:
datasets, typically exhibit robust performance
cases in practice. Here, N denotes the number of available modalities. (b) Our work studies more general scenario, where various modality-missing cases would occur at both training and inference. (c) Performance comparisons between TOP-ReID [9] and Miss-ReID (Ours) on the RGBNT201 benchmark. Abbreviations: R: RGB; N: Near Infrared: T: Thermal Infrared. RNT indicates that evaluating the excludes RGB images from the evaluation, and others omit specific modalities in analogous patterns.
data during inference, RNT
Though exhibiting promising performance, existing multi-modalin ReID methods [16, 11, 13, 12, 17] typically rely on an assumption regarding the modality completeness of data, which may not hold in practice owing to privacy protections, sensor failures security requirements [18]. Specifically, as illustrated in Fig. 1 (a), previous multi-modal RelD models, when trained on modality datasets, exhibit robust performance during inference under modality-complett conditions. However, a critical limitation emerges in practical deployments where various modality- scenarios frequently occur, leading significantly degraded discriminative capabilities compared to idealized benchmarks. To address this challenge, the pioneering works (DENet [19] and TOP-ReID [9]) have conducted the initial investigation into pixel-level and token-level cross-modalit, reconstruction. aiming at handling the incompleteness of inference data. However, this paradigm inherently depends on fully observed multi-modality data for effective training. In real-world scenarios, due to data collection limitations, partially observed data streams will drastically compromise the training efficacy of such reconstruction-basee approaches. This underscores the necessity of developing robust multi-modality ReID model that works without requiring data completeness during both training and inference, ensuring its practical effectiveness in real-world applications.
Fortunately, with the advancements of Vision-Language foundation Models (VLMs) [20-25], their inherent cross-modal understanding capability has showcased transformative potentials in various multi-modality downstream tasks. Especially, by harnessing VLMs' open-world vision-text alignment, text-derived semantic features may effectively compensate for incomplete visual information, enabling training and inference. Based on above insight, we specially
robust solutions for modality-misse deliver a flexible framework tailored for more realistic multi-modality retrieval scenario in this paper, dubbed as Miss-ReID, which is friendly to modality missing scenarios without data-completeness assumption during both training and inference. As shown in Fig. (b), the core of Miss-ReID lies in compensating for missing visual cues through vision-text knowledge transfer driven by VLMs. And the compensatory semantic-aligned textual features specifically excel at mitigating performance decline caused by partial visual modality absence.
modality-missing
Concretely, Miss-Rell mainly consists three collaborative modules: Wemory-basedd Heterogencous Identity Prototype Representation (M-bHIPR) module, Modality-invarian Object Structure Modeling (M-iOSM) module, and Language-driven Missing Modality Completion (L-dMMC) module, as illustrated in Fig. 2. Firstly, M-bHIPH extracts diverse visual features from accessible modalities, and then builds modality-sspeciff memory banks store heterogeneous prototypes for each individual identity, ensuring the preservation of multi-modality characteristies. Afterwards, M-iOSM employs structure-aware query interactions to dynamically distill modality-invarian object structures from existing localized visual patches. By leveraging the texiual inversion technique [26, 27], the extracted visual structural features are further reversed into pseudo-word tokens that encapsulate the identity- relevant structural semantics with L-dMMC module. Ultimately, the inverted tokens, integrated

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with diverse learnable modality prompis, are embedded into crafted textual templates to form the personalized linguistic descriptions for diverse modalities. Benefiting from VLMs' inherent vision- text alignment capability, L-4MMC produces the textual embeddings to substitute the absent visual cues. These compensatory textual embeddings are further optimized through a memory-based contrastive constraint, thereby ensuring vision-text feature consistency. With the collaborations of M-bHIPR, M-iOSM and L-dMMC modules, our proposed Miss-Rell effectively compensates for the information absence caused by incomplete modalities, significantly improving retrieval performance under various modality-missing scenarios against state-of-the-art methods, as illustrated in Fig. I (c). At glance, our major contributions are summarized follows:
ReID under more general modality-missing scenarios encountered during both training and inference. Our Miss-RelD allows the arbitrary modality-missin inputs, while preserving the multi-n molli-modality representation capacity, thereby propelling the advancement of multi-modality RelD toward real-world deployment.
(i) To our knowledge,
the first work to handle
(ii) Bolstered by the inherent vision-text reasoning capabilities of Tision-Language foundation Models (VLMs), Miss-RelD dynamically compensates for missing visual cues through semantic-aligned textual embeddings, and our intriguing findings highlight the potentials of developing VLMs within the realm of Multi-modality ReID encountering incomplete data streams.
(iii) Comprehensive experiments underscore model's efficacy and superiority over state-of-the the-art methods in various boddality-missing retrieval scenarios, and our model demonstrates the lowest performance declines in mAP and Rank-1 accuracy compared modality-complete evaluations several benchmark datasets.
Related Work
Multi-Modality Object RelD: Fueled by the complementary property from different modalities, multi-modality object ReID has drawn escalating research attention recent years. For example, PFNet [28] is first proposed to progressively fuse features from diverse source modalities, enabling the extraction of diseriminative multi-modality representations. EDITOR [11] is proposed to select object-centric tokens for filtering out irrelevant background information. DeMo [12] is designed to balance the decoupled hierarchical features using the mixture of experts, thereby enhancing feature robustness against variations in imaging quality across modalities. IDEA [10] is presented to construct text-enhanced multi-modality object ReID benchmarks, providing a structured caption generation pipeline. However, existing multi-modality studies typically assume the modality integrity during both training and inference, which strictly undermines the retrieval performance in the absence of partial modalities.
Multi-Modality Learning with Missing Modality: Recently, several multi-modality learning methods [29-31, 18, 32-38] have prioritized improving the model's resilience against missing modalities. For instance, SMIL [30] utilizes the Bayesian Meta-Learning to simulate the latent features of missing modalities. ShaSpec [31] is proposed to explore shared-specife feature modeling framework to deal with missing modality in training and evaluation. Lee et al. [18] plug the learnable modality-missine wwwiimmiiiinnnieeee aware prompts into multi- molliitt transformers to identify different modality- missing inputs, thereby adapting the pre-trained transformer for various modalittymissing tasks. Ke el al. [32] propose training-free pipeline to address missing modality completion by leveraging the capabilities of Large Multimodal Models (L.Mss). These advancements inspire us work on completing multi-modalle object representations under modality-missing retrieval scenarios.
3 Method
3.1
Preliminary
For simplicity and generality, we consider multi-modal RelD datasets comprising three modalities: RGB, Near Infrared (NIR) and Thermal Infrared (TIR) modalities. Formally, define modality- complete dataset as Drom = (Irgh. Inary Iurrs V}. where Im = } N.. denotes the set of N.,
images in modality m E {rgb, nùr, tir), and y = {vr) represents the identity labels for each paired triplet sample (L'yto Thers ). Under the modality-missine training paradigm proposed in
3

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Memory-based Heterogeneous
Modellty-ivvvrint
3. Longuoge- driven
identity Prototype Representation (M-aHIPR)
Object Structure Modeling (M-IDSM)
Missing Modolity Completion (L-dMMMC)
ID
RGB Featuris
Structure-awere Queries
Adaplive
Identity Pscudo Word (ID #1)
Learnable Modalit Prompts
Encoddrr
Jiaege
LosEZ
As image-uf of beleesos who skons P uimbatts
Patches
Inversiin Network
An of persesss who skono oie oitrrstts
Push
MHA
perpers shtweethe
NIR Features
LONGR
Image V
Mommny
Inversoo Natwork I
Mamories M-AHIPR
LoWdS
K&V
Identitt Pseudo Word IIDE +k2)
Logis
Til Features
Potches
Meeror
Image Encodia
Text Encoder I
MHA
Aniraage
Push
Lutk'
Animagg of person, whorataa
ottributic
Training Data
Structure Embeddings
The 1-st sample with Identity(in)
MHA
Multi Head
Averaae Pooling
Learnable
Figure 2: Pipeline of the proposed Miss-RelD under miialit training conditions.
this work, we construct a modality modality-incomplee incomplete dataset Dimis Deoms where certain modalities may be absent for specific samples by simulating random modality missing. Specifically, each modality m associated with a missing probability 7mm forming probability tuple 7) (rrgb. Mrir Miir). egg., (0.1, 0.1) indicates a 10% chance of losing any single modality. For each triplet sample (Tight 1i.r Hor y²) in D tensor with probability Tmm or retaining the original I with probability 1 7m Accordingly, the resulting modality incomplete dataset Miis enables us to explore robust multi multi-modality object RelD model under controlled modality absence.
simulate modality absence by randomly setting Inx to zero-pixel
3.2 Memory-based Heterogeneous Identity Prototype Representation (M-bHIPR)
As illustrated in the left of Fig. 2, M-bHIPR first extracts uni-modal visual features from accessible modalities. On that basis, it constructs an independent memory bank for each modality, storing heterogeneous identity prototypes (one per modality) explicitly retain the characteristics of each modality. Technical implementations are elaborated as follows.
Iar)) in modality-missing training dataset Dmis. we capture its corresponding RGB, NIR or TIR visual features first, respec- tively, which is formulated as
Visual Feature Extraction: Given the i-th triplet sample (Trgo= Fir
f Fr 10
Here, V(*(0y) expresses the siamese visual encoder derived from pre-trained VLMs (e.g., CLIP) parameterized by 0y. fi, E lxd and Fi E g. denote the produced global class embedding and local patch embeddings in modality m {rgb, nir,tir), respectively. Nr denotes the number of divided local patches, and is the embedding dimension.
To preserve and dynamically update the hetero- geneous personal characteristies for each identity, we design a hierarchical memory architecture consisting three modality-specifie memory banks. Each bank stores identity-aware prototypes that encode the unique discriminative patterns within their respective modality (RGB, NIR, TIR). The prototype initialization and update protocol is defined follows:
Prototype Initialization and Update Protocol:
For each identity & in modality m, the initial prototype Pm,k computed as the feature centroid of all observed training samples belonging to identity in modality m, formulated as
Etheny She if 7 A
k
(2)
0.
otherwise.
Here, Hk indicates the set of whole available class embeddings with identity & in modality m. f/, denotes the j-th embedding contained in H. and | counts the size of set. Notably, some

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identities may lack samples in specific modalities under higher missing rate, i.e. Hrr = 0. Therefore, the ecro-initialization serves as a placeholder mechanism when no modality-speciie samples exist, maintaining the structural integrity of the memory bank.
After the f-th training epoch, each prototype stored in memory is updated using the corresponding class embeddings to integrate newly discriminative information while retaining historical knowledge in an Exponential Moving Average (EMA) way, written
(+) P Pi..k oPmm,k + (1 - -o) y =k
(3)
Here, a E (0, 1] is momentum coefficient controlling the update smoothness, and is empirically set as 0.2. fm is the newly learned class embedding of the i-th image in modality m with identity k
Critically, each modality-specifie memory bank is updated using features derived exclusively from its corresponding modality. This design explicitly avoids cross-modality feature contamination during prototype refinement, thereby preserving the modality-specifie object discriminative patterns within the respective representation space.
Intra-modality Contrastive Optimization: On top of above established memory banks, the intra- modality contrastive loss function based on ClusterNCE [39] is designed to learn identity-invan invariaat features within each modality. Specifically, for given query fú from modality m, compute its similarity with all identity prototypes stored in the corresponding memory bank, formulated as
N
DE |n'= exp(Si Pmm,k/T)
Nec
log Ek=eep((J exp(Si: Pm,.kT)
where y² is the ground-truth identity label of the i-th sample tuple. Liyi=k] is an indicator function that equals 1 if y (positive pair), and O otherwise (negative pair). K denotes the number of all identities. 7 is temperature hyperparameter controlling the concentration level of the distribution, and is experimentally set to 0.05 here.
This optimization encourages our model to maximize feature-prototype alignment for positive pairs, while simultaneously minimizing cross-identity similarity for negative pairs. Such dual objectives cultivate the learning of identity-invarian features within each modality.
3.3
Modality-invarinn Object Structure Modeling (M-iOSM)
The proposed M-iOSM hinges empirical observation that object structural configurations, e.g., spatial relationships among body parts, or scene element compositions, typically exhibit remarkable consistency across visual modalities. This suggests that structural patterns encode identity-specific information that is preserved regardless of the sensory modality. This key insight motivates our approach: By distilling construct a robust bridge to facilitate subsequent feature completion for missing modalities within the L-dMMC module (Sec. 3.4). Its implementations are elaborated as follows.
-adality-invariant structural knowledge from available modalities, we aim to
Adaptive Structure-aware Querying: As illustrated in the middle of Fig. 2, M-iOSM is designed to distill modality-inne -invariit structural representations by adaptively querying object structural patterns from available modalities through a learnable query-based Multi-Head Attention mechanism (MHA).
In details, we initialize set of N₂ learnable query vectors Q = [9 92. IN,1 IR N.x. that serve as modality-shared "Structural Probes", which are expected to capture diverse structural patterns shared across modalities. Given Q and each input modality m E {rgb, nir, tir) with localized visual patches F E I N, xd derived by Eq. (1), we first project them into query, key and value spaces for each attention head h, respectively, written as
(5) where LN is the operation of Layer Normalization. w E [ddds w E gdxda and W E Rdxdæ are three parameter-independent projection matrices. du = d/Nu denotes the head size, and Nor is the number of heads. After that, the adaptive structure-aware querying can be formulated as
LN(Q)W* K LN(FF, Wh Vith LL((F.)) Ww:
With Softmax m
(("* h m )/ )/Vdzz Sih wi.h vi.h m m
S = Cat(S m , ... SS.NN). (6)
5

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Here, W E NR,,,, normalized by a Softmax function, denotes the weight matrix for perceiving the discriminative structural features from modality in the h-th attention head. S E R xdæ represents the aggregated structural features. Cat(s) means concatenating features from all heads along channel dimension, resulting S. E IR- that represents the modality-invarian spatial structural features from modality
To obtain the information-commplte and modality- invariant structural representations, we employ straightforward yet effective approach to fuse features from all available modalities, expressed as
Modality- Structure Combination:
Ng E E s' M. -, Em. m E {rgb, nir, tir).
Here, si, denotes the intra cittaatydllity fused structural representation, and is assigned as zero tensor modality m is missing. M, denotes the number of available modalities in i-th triplet sample. Ultimately, the inter-modality fused representation s is defined as the distilled structural feature that encapsulates the modality-invariant visual structural contexts of i-th triplet sample.
During training, the obtained structural representation s² is jointly supervised by the label smoothing cross-entropy loss to align structural features with identity labels, and triplet loss to enforce separation among different identities in the structural feature space. Simultaneously, the learnable query set Q is optimized to discover the most discriminative structural patterns through backpropagation, ensuring queries specialize in capturing identity salient structural cues from multi- multi-modality data.
3.4 Language-driven Missing Modality Completion (L-dMMC)
As depicted in the right of Fig. the L-dMMC module proposed to address the challenge of incomplete multi-modality data by leveraging linguistic priors to compensate for missing visual features, and the details are as follows.
Inverted Identity Pseudo-word Generation: To effectively and efficiently encapsulate identity- relevant structural semantics into textual tokens, we leverage a lightweight inversion network that transforms structural features into pseudo-word embeddings. Specifically, given the modality- invariant structural feature s' derived from Eq. (7), an inversion network maps sf into a continuous latent space, i.e.,
w inv I(s))Oz)
where [(*(0z), implemented by Multi-Layer Perceptron (MLP), denotes the inversion network parameterized by 0z. And w E _g is defined as inverted identity pseudo-word that effectively tells identity-specific visual structural contexts.
To complete VIM-compatible textual input, the inverted identity pseudo-word, combined with a set of learnable modality-specific prompts, are embed into crafted textual template. For instance, template like "An image of a [pseudo-word] person, who shows the [modality m/ attributes." is employed to form the personalized linguistic descriptions tailored for specific modality. Wherein, pseudo-word wine I2lxd encodes identity-relevan structural semantics, while prompts Pm E Nx m {rgb, nér, tir). act as modality anchors, guiding the VLMS to interpret pseudo-word within the context of existing or missing modalities.
-odelity-specific Textual Prompting:
Afterwards, the tokenized textual template denoted as (1), , .bx, combined with w and Pm. is fed into the frozen text encoder to obtain textual embedding, formulated as
F T (ft), wine: .m,. t }00))
(9)
Here, Tx) denotes the text encoder derived from pre-trdined VLMs (e.g., CLIP) parameterized by 07. Therefore, fth E IRlxdd is categorized as compensatory textual embedding when modality m missing, or as reconstructed textual embedding when modality ra available.
Memory-based Vision Contrastive Optimization: To ensure the compensatory or recon- structed textual embeddings align with the visual feature space and bridge the semantic gap between visual and textual modalities, we further propose the memory-based text-vision contrastive optimiza- tion strategy formulated as follows:
[iii exp(Fi. Pm.k/T) Ri=l
K
Cm
log
(10)
K 1=
exp( pm,,/
==1

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Here, & denotes a visual prototype vector, which is derived from all observed training samples belonging to identity k in modality m by M-bHIPR module. Notably, the prototype Pm.. dynami- cally initialized and updated using f'm associated with identity l when identity k lacks samples in modality m, with the aim to mitigate data scarcity issues. Conversely, Pm..k remains unchanged if samples are available. This strategy ensures effective representation learning under varying data availability conditions. During training, each für is encouraged to mimic the distribution of visual features corresponding to the same identity within modality m.
Above optimization enhances consistency between compensatory or reconstructed textual embeddings and their ground-truth visual features, thereby mitigating the absence of visual cues through the anignte textual representations under modality modality-missing missing scenarios.
3.5 Overall Objective Function
Beyond the contrastive learning objectives, i.., Nee and DTSy defined in Eqs. (4) and (10), the visual, structural and textual features derived from M-bHIPR, M-iOSM and L-dMMC modules are also optimized through label smoothing cross-entropy losses (denoted C L CCF and triplet losses (denoted as CMTY Lir CTy3 ), respectively, thereby facilitating their identity discrimination. Accordingly, the overall objective function of our Miss-RelD can be given by the following combination:
cm.T
Grtaa = (CTrh + fir --TTT AI + fGE + CCE + >2 >2 L T2v
V
(11)
Here, E (rgb, nir, lir]. d and X2 are hyper-pasameters to balance the contributions of different terms. Notably, the terms CT.T" LCF and CT2y are incorporated into the optimizations after 20 epochs to stabilize the training.
Experiment
4.1 Datasets and Evaluation Protocols
Datasets: To evaluate our method under modality-missing scenarios, we conducted comprehensive experiments on multi-modality object ReID benchmarks (RGBNT201 [28] and RGBNT [40]) by introducing controlled data dropout during both training and inference phases. Specifically, for each modality-compler dataset, we randomly discard the partial data of each modality according to predefined tri-modality missing rates (e.g.. 10% for RGB, 30% for NIR, 50% for TIR) to simulate real-world sensor failures or data corruptions, generating modality-incompler inputs for performance evaluation. Evaluation Protocols: Consistent with conventions in ReID community, two primary metrics- Cumulative Matching Characteristics at Rank-1 (R-1 accuracy) and mean Average Pre- cision(mAP)aar employed to assess model performance under seven inference scenarios: one modality-complete scenario (denoted as RNT, where all data modalities-Reel Near Infrared and Thermal Infrared- are fully available) and six poddality-missing scenarios (denoted as RNT, RNT, RNT, RNT, RNT and RNT). In these modality-missing scenarios, specific modalities in both query and gallery are omitted (e.g., RNT excludes RGB images from the evaluation, and others omit specific modalities in analogous patterns). Notably, we also report Mean mAP and Mean R-1 across the six modality-missing scenarios as the primary indicators for evaluating the model's 's holistic robustness against missing modalities.
4.2 Implementation Details
Our Miss-ReID implemented using PyTorch libraries and runs on a single NVIDIA RTX A6000 GPU with 48GB VRAM. In line with prior works [9, 10]. the pre-trained CLIP [20] is applied for the vision and text encoders. The model is trained in total of 50 epochs, with the L-dMMC module introduced after 20 epochs. We employ the Adam optimizer for training learnable modules, with a learning rate of 5c-3 for modality prompts and 3.5e-4 for others. The text encoder remains frozen throughout training. The number of structure- aware queries (Ng) is empirically set as 16 and 8 on RGBNT201 and RGBNT100, respectively. The length of modality prompts (N₂) is experimentally set as 4 per modality. Xj and X2 in Eq. (1 1) are both experimentally set to 0.1. Additionally, we summarize the overall training procedure Algorithm and illustrate the inference procedure in Fig. 4 under modality- modality-missing missing conditions, which are available in Appendices A.1 and A.2.
7

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Table 1: The impacts of various components, We report the comparison results between different combinations (Model B - F) and the baseline (Model A) under both modality-copplete and -missing training settings on RGBNT201. Here, 'Modality Complete' represents learning the modality- complete data during training, and 'n = (0.1, 0.1, 0.1)" denotes randomly abandoning 10% RGB images, 10% NIR images, and 10% TIR images during training. The evaluations are both conducted across six modality-missing scenarios, and mean mAp and R-l are reported below.
Modules
Complexity
Modality Complete
? n= (0.1,0.1,0.])
Index
M-bHIPR M-IOSM L-MMCC
Params FLOPs
Mean mAP Mean R-1
Mean mAP Mean R-1
A
x
x
X
86.4M
34.3G
48.9
50.4
46.4
47.0
B
/
X
X
86.4M
34.3G
51.1(+2.2)
51.4(+1,0)
47.4(+1.0)
48.0(+1.0)
C
X
/
X
86.4M
34.3G
50.2(+1.3)
52.1(+1.7)
46.9(+0.5)
48.7(+1.7)
DR
V
V
x
86,4M
34.3G
53.3(+4.4)
54.1(43.7)
49.4(+3.0)
49.8(+2.8)
E
X
/
89.6M
43.6G
52.1(+3.2)
53.4(43.0)
47.4(+1.0)
49.7(42.7)
F
V
89.6M
43.6G
54.6(45.7)
55.7(45.3)
50.1(43.7) 51.3(44.3)
4.3
Ablation Study
As reported in Table 1, we conducted extensive ablation studies on RGBNT201 to evaluate the efficacy of individual components within Miss-Rell Hiss, under both modality-complete and modality-missing training settings.
The baseline method (Model A) relies solely on class embeddings derived from available modalities by visual encoder for retrieval, achieving 48.9% Mean mAP and 50.4% Mean R-1 accuracy under modality-complete training. When trained with 10% modality missingness (n) (0.1, 0.1, 0.1)), its performance drops 46.4% Mean mAP and 47.0% Mean R-1, highlighting the challenge of modality incompleteness.
Baseline Settings:
Effectiveness of M-DHIPR: Integrating the M-bHIPR module (Model B) improves modality- complete mAP by 2.2% (51.1%) and modality-missing mAp by 1.0% (47.4%). This indicates that M-bHIPR effectively aligns features with identity-related prototypes within each modality, enhancing discriminability while preserving modality-speciff characteristics.
Effectiveness of M-iOSM: The proposed M-iOSM module (Model C) alone contributes 0.5% Mean mAP (46.9%) and 1.7% Mean R-1 (48.7%) improvements under moodality-missing setting. When combined with M-bHIPR (Model D), performance surges to 49.4% Mean mAP (+3.0%) and 49.8% Mean R-1 (+2.8%). This indicates that M-iOSM fosters cross- coss-modality interaction by dynamically mining similarities across modalities and modeling modality-invarian complementing M-HHIPR's ' intra-modality refinement. Notably, the M-bHIPR and M-iOSM modules operate without additional computational overhead during inference, preserving inference efficiency while enhancing multi-modality feature alignment.
object structures,
Effectiveness of L-dMMC: The proposed L-dMMC module (Model E) alone yields 1.0% mAP (47.4%) and 2.7% Rank-1 (49.7%) gains under modality-missine training. Combined with all modules (Model F), it achieves the excellent performance: 54.6% Mean mAP (+5.7%) and 55.7% Mean R-1 (+5.3% in modality-comppl (+4.3%) under missingness. L-dMMC leverages language priors to compensate missing modalities bolstered by the inherent vision-text reasoning capabilities of VLMs, enhancing robustness to various modality-missing scenarios. While parameters increase from 86.4M (Model A) to 89.6M (Model F), and FLOPs rise from 34.3G to 43.6G, the trade-off is justified by substantial accuracy improvements.
setting, with 50.1% Mean mAP (+3.7%) and 51.3% Mean R-1
4.4 Comparisons with the State-of-the-art Methods
We benchmark our Miss-RelD against several state-of-the-an methods, including PCB [1], TOP- ReID [9], DeMo [12] and IDEA [10], under modality-compler training and modality-missingg inference scenarios. Tables 2 summarizes the main results for multi-modality person RelD, evaluated on the RGBNT201 datasets. is evident that our proposed Miss-RelD demonstrates significant robustness and superiority over SOTA methods in handling modality-missing challenges during inference. Specifically, Miss-RelD consistently outperforms the other methods in terms of both mAP and R-1 across the most modality-missie scenarios. Crucially, in the most challenging scenario
8

---

Table 2: Performance comparisons under missiig situations that only occur at the inference phase of moliiityyy person RelD on RGBNT201. f denotes the model that trained using both
images and their corresponding text annotations. The best results are labeled with boldface. 4x.x%
and Lx.x% % highlight the lowest mAP and R-1 drop rates, respectively. indicates that the metric is unpublished.
RNT
RNT
RNT
RNT
RNT
RNT
RNT
Mean
Methods
mAP R-1
mAP
R-1
mAP
R-1
mAP
R-1
MAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
PCB
23.6 24.2
24.4
25.1
19.9
14.7
20.6 23.6
11.0
6.8
18.6
14.4
19.7 18.1
32.8 28.1
[BCCV 2018
128.0%
[20.6%
39.39%
147.7%
37.2%%
166.5%
13..%%.48..
19..%%
TOP-ReID
54.4 57.5
64.3
67.6
51.9
54.5
35.3
35.4
26.2
26.0
34.1
31.7
44.4
45.4
72.3 76.6
[AAAT 2024]
(24.89..24.99%)
128.296
16..8%-666.11
159
152.8%
158.68
1.88
188.6% 140.7%
DeMo
63.3 65.3
72.6
75.7
56.2
54.1
45.6
46.5
26.3
24.9
40.3
38.5
50.7
50.8
79.0 82.3
[AAAT 2025]
[19.99.j20.7%
18.1% 18.0%
[28.999
334.39
(42.3%.443.59
(66.79-U69979
149.0%
153.2%
J33.899-338.99
IDEA
62.9
71.5
58.4
43.3
27.1
39.9
50.5
80.2 82.1
ICVPR 2025]
121.6% 1-%
[10.8%
L-%
127.25
4-56
|46.0%
4-%
166.29
4-5%
150.25
4-56
137.0%
1-%
Relss
66.6
68.2
72.4
75.5
63.2
63.8
47.2
49.5
34.5
33.3
43.9
44.3
54.6
55.7
76.9
78.9
[Ours]
(13.4%
613.6%
15.9%
14.3%
117.8%
119.1%
38.669
(55.19.557.8
142.9%
443.9%
129.09-.29..
where both RGB and TIR images are missing (RNT), Miss-RelD achieves 34.5% mAP and 33.3% R-1, which are significantly higher than those of the other methods. Notably, the colored boxes highlights Miss-RelD's ' dominance in minimizing performance decay under diverse missing-modaling combinations. For instance, in RNT scenario, Miss-ReID experiences only 13.4% drop in mAP and 13.6% drop in R- I, which are the lowest drop rates among all compared methods. Moreover, Miss-RelD achieves new state-of- thet-ettttt average performance (54.6% mAP, 55.7% R-1), surpassing the second-best DeMo by 3.9% mAP and 4.9% R-1. The superior performance of Miss-RelD can be poodality-missing situations. Unlike other
attributed its textual feature completion tactic in handling methods that may heavily rely on the presence of all modalities during both training and inference, Miss-RelD designed to adaptively compensate missing modalities, maintaining high performance even when some modalities are unavailable. This makes Miss-RelD more practical and reliable solution for real-world applications, where modality completeness cannot always be guaranteed. Moreover, the exhaustively comparative analysis with SOTA methods for modality-missii vehicle ReID on RGBNT100 datasct are provided in Appendix A.3.
4.5 Performance Analysis of Miss-Rell Under Varying Tri-modality Missing Rates
We conduct a comprchensive evaluation of our model's robustness tri tri-modality missing rates 17 (Trgh. Teir ) in multi-modality person ReID on RGBNT201. Table 3 reports performance under varying degrees of missing modalities during training, while assessing the model's 's behaviors in both modality-comples (RNT) and modality-missingg (e.g., RNT, RNT, etc.) scenarios during inference. As expected, increasing the missing rates inevitably degrades the model's performance across most evaluation scenarios, suggesting sensitivity to missing modalities during training. Nevertheless, the Mean row demonstrates that the model's average performance degrades gracefully as the missing rate increases, with mean mAP dropping from 54.6% in ideal case when v (0.0, 0.0, 0.0) to 47.3% in extreme case when 7 = (0.5, 0.5, 0.5). This suggests that our model maintains reasonable robustness even under high missing rates, unlike some existing models (e.g., PCB [1] with 19.7% mAp and TOP-ReID [9] with 44.4% mAP) trained exclusively on modality-compleet data, which often struggle with missing modalities during inference.
4.6 Structure-aware Query Attention Region Visualization
As exhibited in Fig. 3, to intuitively showcase the efficacy of structure-aware queries in M-iOSM module, we specially provide visualizations about the most attentive region of each well-learned query vector across diverse scenarios. Each attentive region highlighted by translucent mark (Q1-Q16) to facilitate intuitive interpretation, revealing two key insights. Firstly, within each modality, the distinct

---

Table 3: Performance comparisons of setting different tri-modality missing rates on RGBNT201. Each tuple (Argb, Thir. T/(rr) represents the proportion of randomly abandoned RGB, Near- Lear-Infrared, and Thermal-Infrane images during training.
Trd-Mttllity Missing Rate !
(0.0, 0.0, 0.0)
(0.1, 0.1, 0.1)
(0.3, 0.3, 0.3)
(0.5, 0.5, 0.5)
(0.1, 0.3, 0.5)
(0.5, 0.3, 0.1)
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
RNT
76.9
78.9
72.3
73.4
68.4
71.2
68.2
72.8
69.6
72.2
67.6
67.6
RNT
66.6
68.2
61.3
61.7
$7.6
58.3
56.7
58.4
56.1
58.4
57.6
58.4
RNT
72.4
75.5
68.8
72.6
65.8
69.5
63.6
65.1
66.9
69.7
65.7
67.0
RNT
63.2
63.8
55.3
55.7
52.3
56.5
52.3
54.4
53.2
57.3
50.2
50.7
RNT
47.2
49.5
42.8
45.2
44.9
47.5
41.6
40.8
41.1
40.3
47.1
47.6
RNT
34.5
33.3
30.9
29.7
26.8
26.3
26.5
22.2
27.1
28.1
26.5
24.6
RNT
43.9
44.3
41.5
42.7
43.0
45.5
42.7
46.4
43.9
47.0
39.2
38.8
Mean
54.6
55.7
50.1
51.3
48.4
50.6
47.3
47.9
48.1
50.1
47.7
47.8
RNTT
RNTT
ANT
Figure 3: Visualizations of the attentive regions towards 16 well-learned structure-aware queries. We provide the results in modality-missing cases (a-f: RNT, RNT, RNT, RNT, RNT and RNT), and modality-complete scenario (g: RNT) sampled from RGBNT201 dataset.
structure-aware queries exhibit specialized focus on semantically meaningful body regions, e-g., joint positions, accessory locations, etc. Critically, despite modality disparities, the object structure cues that excavated from different modalities with identical identity maintain contextual consistency, as displayed in Fig. 3 (d-g). These observations validate the robustness of our proposed M-iOSM module for probing the modality-invarian structure cues under real-world retrieval scenarios.
4.7 Further Evaluations and Analysis
To comprehensively evaluate the efficacy of our method, additionally conduct extensive experi- ments, encompassing more comparative evaluations, feature distribution visualizations, performance evaluations under 49 real-world inference scenarios, ranking lists, etc. Please refer to the Appendix A for detailed results.
Conclusion
In this paper, a novel multi-modality object re-identification framework (Miss-ReID) has been proposed to furnish the robust retrieval under modality-missine missing conditions without requiring the data completeness during either training or inference. By harnessing the open-world vision-text alignment capabilities of Vision-Language foundation Models (VLMs), text-derived semantic features nominated to effectively compensate for incomplete visual information, enabling robust solutions for modalitt-missing training and inference. Extensive experiments validate model's efficacy and superiority over state-of caeee-ffthe--eee fi-the-an art methods across diverse modality modalltt-missing missing scenarios, advancing the practical deployment of multi-modaliyy object re-identification.
Limitations: While the current Miss-ReID demonstrates the significant robustness across various modality-missin scenarios compared to state-of-the-art methods, further works are required to improve resilience extreme cases (e.g., complete modality collapse) and expanded modality integration (e.g., event/LiDAR data, sketches, audio).
10

---

Acknowledgments and Disclosure of Funding
I would like to extend my sincere gratitude to my doctoral supervisor, Prof. Qiang Zhang, for his patient guidance and valuable contributions to this work. This work was supported in part by China Postdoctoral Science Foundation under Grant 2023M742745, in part by the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF) under Grant GZB20230559, in part by Ba- sic and Applied Basic Research Foundation of Guangdong Province under Grant 2023A1515110165 in part by the National Natural Science Foundation of China under Grant 61773301 and Grant 61 603290,, in part by the Fundamental Research Funds for the Central Universities under Grant No.2YTS24022, and in part by the State Key Laboratory of Reliability and Intelligence of Electrical Equipment (Hebei University of Technology) under Grant EERI- KF2I--K50020055.
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A
Appendix
In this supplementary material, we provide additional experimental evaluations, in-depth analyses and abundant visualizations to support our findings, and the structure is organized as follows:
A.1
Training Procedure of Miss-RelD
A.2 Textual Completion During Inference
A.3
Comparative Analysis with SOTA Methods for Modality Vehicle RelD
A.4 Retrieval Performances under 49 Real-world Scenarios in Tri-modality RelD
A.5 Feature Distribution Visualization
A.6 Retrieval Results Under Both Modality-complle and -missing Situations
A.1 Training Procedure Miss-ReID
We summarize the overall training procedure of our Miss-RelD under modality-missing conditions in Algorithm 1, where we explicitly consider three representative modalities, i.e., RGB, Near Infrared (NIR) and Thermal Infrared (TIR), the example.
Algorithm 1: Training procedure of Miss-ReID.
Input: Complete Multi-modality Object RelD Dateset: Deom {Irgbs Inir. Itir:y]:
Modality-missing Rate: = (Argb Unir; Mrir); Learnable Structure-and aruuture-aware Queries: Q: Learnable Modality Prompts: Pn, m E {rgb, nir, lir); Total Training Epoch: E.
Output: Robust Object ReID Model Under Modality-missing Conditions.
Build modality-incomplete dataset Diis C Deoo by randomly dropping according y
2 Initialize memory bank for each modality by Eqs. (1) and (2);
for epoch=1:F do
# Memory-based Heterogeneous Identity Prototype Representation.
Extract multi-modality features of Mis by image encoder V from pre-trained VLMs; Update heterogeneous identity prototypes stored in memories in an EMA way by Eq, (3);
Calculate L
using Eq. (4); Calculate C and LCE
v..V
nir, nrgb,nir, tir).
# Modality-invariiie Object Structure Modeling.
Employ Q to adaptively query modality-invarie structure embedding from available modalities using Eqs. (5-7); Calculate C and L.
10
epoch > 20 then
# Language-driven Missing Modality Completion.
12
Invert into identity pseduo-word Wine by inversion network I using Eq. (8); Form three modality-specific text descriptions that incorporating Wine and Pmi Generate textual features by forzen text encoder T from pre-trained VLMs using Eq. (9);
13
14
Caleulate C..TT and L.m.T" m {rgb, nir, tir);
15
16
Calculate LT2V using Eq. (10), m E {rgb, nir, tir).
17
else
18
let CT = 0, CT. = 0 and S = 0, m E {rgb, nir, lir).
19
end
20
Optimize network by minimizing Eq. (11).
21
end
A.2 Textual Completion During Inference
Under various modality modality-missing inference scenarios, we substitute missing visual cues with seman- tically aligned text embeddings, thereby fostering robust multi-modality representations. Here, as illustrated in Fig. 4, we consider the scenario where RGB images are missing as an example to detail above process. Firstly, the visual features are captured from each available modality (i.e., NIR and TIR) via VLMSs fine-tuned image encoder. Subsequently, the modality-invarian object structure cues probed from two groups of local visual patches, and are further reversed into
14

---

Mocalty-invaiint Structure Feature
Well-eermed RGB Modality Prompt
RGB
NIR
TIR
NIR Features
Image Encoder
Inversion Network
Text Encoder
Features 2
Foatures
Identity Pseudo Word
Text Templote
Compensatory RGB Features
RGB Modality Missing
Object Structure Modeling
Illustration of our proposed textual feature completion tactic for missing modalities. Here, we take the absence of RGB image as an example.
Figure 4:
Table 4: Performance comparisons under modality-missing situations only occurred at the inference phase of multi-modality vehicle ReID on RGBNTI00. '-' indicates that the metric is unpublished.
RNT
RNT
RNT
RNT
RNT
RNT
RNT
Mean
Methods
mAP R-1 mAP
R-1
MAP R-1
mAP
R-1
mAP R-1
mAP R-1 mAP R-1
mAP R-1
DENet
62.0
85.5
56.0
80.9
50.1
74.2
68.1 89.2
JarXiv 2023]
19.0% 14.1%
117.8% 19.3%
126.4%
116.8%
TOP-ReID
70.6
90.6
77.9
94.5
64.0
81.5
42.5
69.3
45.9
65.4
55.4
77.8
59.4
79.9
81.2 96.4
[AAA] 2024]
113.1%
16.0%
14.1%
12.0%
21.2%
115.5%
U47.7%
128.1
143.5%
132.2%
131
119.3%
126.8%
117.1%
DeMo
81.0
94.5
84.1
96.5
71.1
87.6
50.2
73.7
59.6
78.1
66.3
82.8
68.7
85.5
86.2 97.6
|AAAI 20251
16.0%%
13.2%
12.4%
11.19
17.55%
110.25
J41.8%
124.5%
130,95%
120.0%
(23.1%
115.2%
120.3%
112.4%
Miss-Ras ReID
77.3
94.9
81.9
96.3
70.3
89.7
47.1
78.0
57.7
78.2
65.6
86.8
66.6
87.3
82.5 96.7
[Ours]
16.3%
91.9%
10.7%
L0.4%
14.8%
17.2%
U42.9%
19.33%
130.1
19.11%
120.5%
10.2%% 119.3%
99.7%
pseudo-word that encapsulates the identity-related visual structural contexts. Ultimately, integrated with well-learned RGB modality prompts, inverted token are inserted into text template to form the input for VLMs' frozen text encoder. Benefiting from VLMs' inherent image-text text alignment capability, the resulting textual features serve as the compensatory features for missing RGB modality. Therefore, by concatenating the existing NIR and TIR visual features with the compensatory RGB textual features, we enable robust multi multi-modality object re-identification under modality-missing inference condition.
A.3 Comparative Analysis with SOTA Methods for Modality Vehicle ReID
We also benchmark our Miss-RelD against several sfate-of-the-aae methods, including DENet [19], TOP-Rell [9] and DeMo [12]. for multi multi-modality vehicle ReID under modality-compllete training and modality-missinn inference scenarios. Notably, the textual template in L-dMMC module crafted as "An image of [pseudo-word] vehicle, which shows the [modality m] attributes" here. Tables 4 reports the main results evaluated on the RGBNT201 datasets. Obviously, th proposed Miss-RelD SOTA methods across diverse modality-
demonstrates superior performance and robustness missing combinations. To be specific, Miss-RelD exhibits minimal performance degradation in critical scenarios: under NIR-missing case (RNT), its R-1 accuracy drops by only 0.4% (96.3% vs. 96.7%), outperforming DeMo's 1.1 % decline, TOP-ReID's 2.0% drop and DENet's i 4.1% reduction; under TiR-missing case (RNT), it maintains
R-1 accuracy decrease of 7.2% (89.7% vs. 96.7%), surpassing DeMo (10.2% and TOP-ReID (15.5%) by 30% to 50%; even in the most challenging case where both RGB and TIR images are missing (RNT), achieves 78.2% R-1 accuracy (19.1% drop), exceeding DeMo (78.1 1% with 20.0% drop) and TOP- (65.4% with 32.2% drop). Across all scenarios, Mils-ReID achieves 66.6% mAP and 87.3% 57.3% R-1 with degradation rates of 19.3% (mAP) and 9.7% (R-1), outperforming DeMo 300.3%/12.44% and TOP-ReID (26.8% 33%/1....% (7.1%) in generalization under modality uncertainty. Compared DeMo, Miss-ReID shows smaller degradation in 5/6 mondality-missing scenarios in terms of R-1 accuracy, while outperforming TOP-ReID by reducing average degradation by 7.2% (mAP) and 7.4% (R-1), These results validate effectiveness of our language-driven missing modality completion approach, which enables Miss-RelD to serve as a robust. molti-moddlity vehicle RelD solution for real-world deployments where partial modality failures frequently occur.
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Figure 5: Matrix visualizations of the retrieval performances, i.e., (a-b) Mean mAP, and (c-d) Mean Rank-1, on the RGBNT201 dataset. Here, the baseline model and our Miss-ReID are both trained using modality-missinee data with (0.1, 0.1, 0.1), and are evaluated under I modality-compler inference scenario MONT-to-RNT"" and else 48 more general modality-missing inference scenarios.
A.4 Retrieval Performances under 49 Real-world Scenarios in Tri-modality RelD
To comprehensively evaluate the robustness of our Miss-RelD towards incomplete modalities, we consider more general EnverytooGaalleyy retrieval scenarios, encompassing 1 modality codalltt-complete case ("RNT-to- WeTT-oo-RN fo-RT"") and else 48 modality-missing cases. As illustrated in Fig. 5, we visualize the performance comparisons between the baseline model (a, c) and our Miss-ReID (b, d). It's evident that our Miss-RelI outperforms the baseline model in both modality-complete and -missing scenarios, as evidenced by the higher Mean mAP and Mean Rank-1 accuracy. Specifically, in the moodality-compled case, our model demonstrates superior performance, indicating its effectiveness in leveraging all available modalities for accurate retrieval. Furthermore, even in else modality-missiiee cases, which represent more challenging and realistic scenarios, our model consistently achieves better retrieval results compared to the baseline. While it is true that certain extreme moddality-missing scenarios (e.g.. "RNT-to-PE to-RNT" and "RNT-to-RNT") still maintains an advantage over the baseline model. This highlights the robustness of our approach but also points an area for future improvement. Enhancing the model's ability to handle extreme modality missing will be a key focus in our future work, further boosting retrieval performances in these challenging cross-modality conditions.
model's performance is somewhat limited,
A.5 Feature Distribution Visualization
As shown in Fig. 6, to intuitively witness the efficacy of the compensatory textual features derived from I.4MMCC module, we visualize the distributions of three types of discriminative features under challenging modality-missinn case (RND, where both RGB and TIR images unavailable. From
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Concatenating RGB Textual Features
[c] Concatenating TIR Textual Features
Figure 6: The feature distributions of 10 identities randomly sampled from the RGBNT201 dataset by using t-SNE [41]. Here, we take the challenging case (RNT) that both RGB and TIR images unavailable as an example. (a) The vision-only features derived from available NIR images. (b) The fused features that concatenating the compensatory RGB textual features on (a). (c) The fused features that further concatenating the compensatory TIR textual features on (b). Different colors refer to different identities.
Fig. 6 (a) to Fig. 6 (c), the features of challenging samples (specifically, IDs 270, 277, and 291) become increasingly compact, while the separation between different identities (IDs) widens. These visualizations fully substantiate that progressively incorporating the compensatory RGB and TIR textual features into limited visual features significantly enhances the feature discrimination and robustness towards moodllttymissiing cases.
A.6 Retrieval Results Under Both Modality-complete and -missing Situations
As shown in Fig. 7, we compare the ranking lists generated by (a) the baseline model and (b) our proposed Miss-ReID, under both modality-complete and modality- inference scenarios. The baseline model demonstrates limited performance, yielding high number of incorrect matches, particularly in the most challenging modality-missiine cases (RNT, RNT, and RNT). In contrast, Miss-Red ReID achieves superior performance, with significantly fewer incorrect matches and more accurate results. These findings intuitively validate the effectiveness of our approach in compensating for missing modalities using textual features.
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Figure 7: Ranking list comparison between (a) Baseline and (b) Our Miss RelD under one modality- complete retrieval scenario and 7 modality- retrieval scenarios. The green box denotes the correct match, whereas the red box signifies the incorrect match.
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25


Reviews:
[{"review_id": "CMlHBDHLZN", "reviewer": "Reviewer_z2ZA", "summary": "This paper presents a flexible framework tailored for a more realistic multi-modality retrieval scenario, dubbed Miss-ReID. Miss-ReID aims at compensating for missing visual cues via vision-text knowledge transfer driven by Vision-Language foundation Models (VLMs), effectively mitigating performance degradation.", "questions": "1. Both [18] and [32] address modality-missing scenarios during training/inference. Concretely clarify the fundamental distinctions between Miss-ReID and these works, and supplement comparative experiments.\n2. The M-iOSM module assumes cross-modal invariance of object structures, yet significant contour discrepancies may occur under low-light conditions (e.g., RGB vs. TIR). How to guarantee truly modality-invariant structural features? Could this introduce noise?\n3. Current experiments only simulate random modality missing. How would Miss-ReID maintain robustness when encountering inter-modal dependent missing (e.g., NIR invariably missing when RGB is unavailable)?\n4. As shown in Table 2, Miss-ReID’s mAP under full modalities (RNT) (76.9%) lags behind DeMo (79.0%) and IDEA (80.2%). Does the textual compensation module introduce interference to visual features when modalities are complete?  How to optimize this?\n5. L-dMMC increases FLOPs by 23% (Table 1). Are there plans to develop a lightweight variant(e.g., via prompt distillation) for edge devices? What is the actual inference speed?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1.  Miss-ReID handles multi-modality ReID under more general modality-missing scenarios encountered during both training and inference. Miss-ReID allows the arbitrary modality-missing inputs, while preserving the multi-modality representation capacity.\n2. Bolstered by the inherent vision-text reasoning capabilities of Vision-Language foundation Models (VLMs), Miss-ReID dynamically compensates for missing visual cues through semantic-aligned\ntextual embeddings, and our intriguing findings highlight the potential of developing VLMs within the realm of multimodal ReID, encountering incomplete data streams.\n3. Comprehensive experiments underscore the model’s efficacy and superiority over state-of-the-art methods in various modality-missing retrieval scenarios\nWeakness:\n1. The paper claims to be the \"first work to handle modality-missing scenarios in both training and inference\" (intro). However, prior works like Lee et al. (2023) [18]  and Ke et al. (2025) [32] have addressed missing modalities in both phases—e.g., via learnable modality-missing-aware prompts or LMM-based completion. The distinction between Miss-ReID and these methods requires clearer articulation to justify its \"first work\" claim.\n2. The structure-aware querying mechanism lacks critical implementation specifics, including undefined initialization/optimization objectives for learnable vectors Q, no explicit loss (e.g., cross-modal alignment) ensuring structural consistency across modalities (e.g., RGB-TIR feature alignment), and absent quantitative validation of said consistency.\n3. The L-dMMC module increases FLOPs by 27% (34.3G → 43.6G, Table 1) but omits: Inference latency metrics (critical for real-world deployment, e.g., surveillance systems)\n4. Textual compensation efficacy lacks fine-grained validation in terms of VLM backbone comparisons (e.g., CLIP vs. BLIP-2) or template designs. At the same time, the contribution of pseudo-word generation is only qualitatively demonstrated (Fig. 6), lacking quantitative ablation studies, such as performance drops without the inversion network.", "was_revised": true, "final_justification": "The author's response has addressed my questions. I am willing to raise my score.", "rebuttal": "We wish to convey our sincere thanks for your valuable comments. Your support means a great deal to us, and we would be truly honored if you would act as an advocate for our work.\nBelow is our comprehensive, point-by-point response to all the identified Weaknesses (W) and Questions (Q). \n\n---\n \n**W4/Q1: About the description of ''the first work''.**\n\nWe sincerely apologize for the insufficient clarity in our original description.\nStrictly speaking, our \"first work\" claim specifically refers to the **multi-modality ReID task**, where no prior work has addressed modality missing in both training and inference.\nWe sincerely acknowledge that [18] and [32] have explored modality-missing scenarios across training and inference, but their focus differs fundamentally from ours.\n\nSpecifically, [18] and [32] primarily address tasks like multi-modal classification or image/text generation, where the core challenge lies in **label prediction or content synthesis**. In contrast, our work targets multi-modality ReID, as a retrieval task requiring **fine-grained identity discrimination**, which demands capturing subtle inter-identity differences rather than coarse-grained classification/generation. We will add these distinctions into the Related Work in the revision.\n\nGiven the task and technical disparities, we are actively adapting [18] and [32] to the multi-modality ReID setting (e.g., modifying their frameworks for identity retrieval) and will include comparative experiments in the revision for comprehensive benchmarking.\n\n---\n\n**W5/Q2:  Some issues about structure-aware querying mechanism in M-iOSM module.**\n\nWe sincerely apologize for unclear descriptions about structure-aware querying mechanism, and our detailed responses are as follows.\n\n  _**a. Optimization objective for learnable vectors Q**_: Learnable vectors Q are optimized via two main types of constraints: 1) **Direct** identity loss and triplet loss ensure structural features are identity-discriminative. 2) **Indirect** text-image contrastive loss from L-dMMC ensures that the structural features, when transformed into pseudo - words through the inversion network, can effectively encapsulate the visual content. The combination of these two types of constraints guarantees that the structural features **not only carry unique identity information** but also **maintain a strong connection with the corresponding textual descriptions**, facilitating cross-modal understanding.\n\n  _**b. Structural consistency constraints**_: Due to imaging technologies or conditions, a small number of multi-modality images may have subtle structural differences or modality-specific noise (e.g., TIR thermal blur). **Forcing the structural features to be strictly consistent may instead lead to the model's overfitting on noisy. To avoid this problem, we eschew rigid alignment by averaging structural features across different modalities to form the final structural representation.**\nThis \"consensus-based\" approach mitigates outlier impacts and leverages cross-modality complementarity, maintaining the stability and reliability of the overall structural feature representation.\n\n  _**c. Reliability of structure features obtained under low-light conditions**_: As mentioned above, our module mitigates the impact of low-quality modalities by averaging the structural features from different modalities. However, further improvements are still needed. As future work, the improvements will be as follows:\n\n - We plan to employ some pre-processing techniques specifically designed for low-light images. These can boost visual and semantic features while preserving details in low-light images, improving input quality for M-iOSM to enhance structural feature reliability.\n\n- We plan to integrate attention mechanisms into M-iOSM, such as multi-scale attention. This will help the model focus on reliable structural cues, reduce noise/inconsistency impacts (especially in low light), and adaptively weight feature parts to prioritize identity-discriminative regions.\n\nWe will elaborate on these details and add more analysis in the revision.\n\n---\n\n**W6/Q5: Model complexity and inference speed.**\n\nWe appreciate your valuable concerns about computational complexity and inference speed, which are critical for real-world deployment. As suggested, we’ve added detailed comparisons of model complexity and inference speed between Miss-ReID and SOTA methods, along with in-depth analyses.\n_However, due to the 10,000-character limit, the detailed response to this issue can be found in Reviewer uuXE (Q1)._\n**We sincerely apologize and appreciate your understanding.**\n\n---\n\n**W7:  Some supplementary ablation studies.**\n\n  _**a. CLIP vs. BLIP-2**_: \nWe have evaluated various VLMs as backbones (**with only text encoders frozen during training**), and the results are presented in Table below.\n|Backbone| Image Encoder| Text Encoder| Params (Trainable)| Params (Total)| Mean mAP|Mean R-1|\n|:------------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n| CLIP    |  ViT-B/16  | Transformer| 89.6M   |   153.0 M   |  54.6  | 55.7|\n| BLIP-2 |  ViT-L/14  | OPT-2.7B    | 310.6M |      3.1B     |  55.4  | 56.4 |\n\nThe table shows BLIP-2, with 3.4× more trainable params and 20× total params than CLIP, yields marginal gains (mAP +0.8%, R-1 +0.7%), indicating stronger VLMs don’t guarantee proportional improvements.\nThis may due to that BLIP-2’s 3.1B params need larger data to realize its potential, and BLIP-2 excels at generative tasks, while CLIP’s contrastive pretraining better fits cross-modality retrieval, offering efficient features for ReID.\n\n\n_**b. Template design**_: \nTo validate the effectiveness of the template design, we have conducted ablation experiments on template variants, as shown in Table below.\n\n|Variants|Mean mAP|Mean R-1|\n|:------------|:-----:|:-----:|\n| T-A (*w/o* Modality Prompt) |     53.9 |   54.8   |\n| T-B (Fixed Modality Prompt) |     54.0 |  55.0   |\n| T-C (Learnable Modality Prompt) | 54.6  | 55.7|\n\n- T-A ('_An image of a [Inverted Pseudo-word] person._') omits modality prompts entirely, relying only on identity pseudo-words;\n\n- T-B ('_An image of a [Inverted Pseudo-word] person in visible/near infrared/thermal infrared spectrum._') uses fixed descriptive phrases (e.g., \"in visible spectrum\") tied to modalities;\n\n- T-C ('_An image of a [Inverted Pseudo-word] person, who shows the [Learnable Modality Prompt] attributes._') employs learnable prompts that adaptively capture modality-specific semantics.\n\nThe performance of T-A shows modality prompts are critical for cross-modal alignment. The weak improvement by-B confirms modality-specific guidance helps, but rigid templates lack contextual awareness. T-C outperforms T-A and T-B, confirming learnable prompts’ superiority. This result indicates the modality-specific learnable prompts dynamically adapt to pre-trained VLMs’ semantic space, aligning better with VLM’s inherent knowledge than rigid fixed phrases.\n\n_**c. Contribution of Inversion Network**_: \nWe apologize for the lack of quantitative analysis on the inversion network. We have conducted ablation experiments comparing performance with and without the inversion network, as reported in Table below. \n\n|Variants|Mean mAP|Mean R-1|\n|:------------|:-----:|:-----:|\n| *w/o* Inversion Network  | 53.7  | 54.8|\n| w/      Inversion Network  | 54.6  | 55.7|\n\nThe result shows that removing the inversion network leads to certain performance drop, underscoring its necessity in **bridging visual-textual gaps**. It can be seen that, by converting structural visual cues into textual embedding, it enables more precise cross-modality alignment, enhancing the L-dMMC module’s ability to generate identity-specific compensation semantics. \n\nWe will supplement these ablation studies with more analysis in the revision.\n\n---\n\n**Q3: The robustness of model when encountering inter-modal dependent missing.**\n\nWe appreciate this insightful question. While current experiments primarily simulate random modality missing, they do cover diverse missing combinations including simultaneous RGB/NIR failure at night. \nOur Miss-ReID enhances discriminative ability by generating textual features for missing NIR and RGB, to some extent.\nTo better address inter-modality dependent missing, future work will create real modality-incomplete datasets (extensively covering such correlated patterns as suggested) and design adaptive fusion modules to explicitly model cross-modality failure correlations, ensuring more reliable feature compensation.\n\n---\n\n**Q4:  Does the textual compensation module introduce interference to visual features when modalities are complete?**\n\nWe sincerely apologize for our unclear description about Table 2. We clarify that Miss-ReID's textual compensation module (L-dMMC) selectively activates only for missing modalities during inference. The 76.9 mAP in Table 2 refers to **visual-only performance** under full modalities (RNT). We also supplement comparative results with/without using textual features in the Table below.\n\n||mAP (complete)|Mean mAP (missing)|\n|:-----:|:-----:|:-----:|\n| DeMo |  79.0    |  50.7 |\n| IDEA   |  80.2    |  50.5 |\n| Miss-ReID (*w/o* text) |  76.9    |  53.3 |\n| Miss-ReID (*w/* text)  |  77.7|  54.6|\n\nThe results indicate that adding textual features improves mAP to 77.7%, confirming textual features do not interfere but enhance discriminability in modality-complete inference. \nMoreover, DeMo and IDEA excel in complete modalities thanks to their complex multi-modality interaction designs, **which heavily depend on on modality-complete inputs**, rendering them fragile when modalities go missing.\nWe will add these details in the revision.\n\n---\n\nWe’re deeply grateful for your insightful feedback, which has significantly strengthened our work.\nWe trust our responses have thoroughly addressed your concerns, and we’d be privileged to have your support for our submission. Thank you for your time and expertise."}, {"review_id": "j1DOl1FXd1", "reviewer": "Reviewer_pLpE", "summary": "This paper addresses the challenge that existing multi-modality ReID models degrade significantly when handling modality-incomplete inputs, proposing a flexible framework called Miss-ReID—the first to support both modality-missing training and inference. Experiments validate its superiority over state-of-the-art methods in various modality-missing scenarios, advancing multi-modality ReID toward real-world applications.", "questions": "1.\tThe novelty of the proposed method is not well-explained. It doesn't mean that this paper lacks novelty. I hope the authors can explain more details about the motivation of model design.\n\n2.\tHow much is the performance difference when different hyper-parameters are used in the overall objective function?\n\n3.\tThere are many sota methods (such as [1], [2], and [3]) in the field of person ReID. Why aren’t they adopted for comparison? \n\n4.    This paper is well presented, but it still lacks significant contributions. If the authors can well solved my questions, I will raise my rating to more positive one.\n\n\n[1] Yu, Chenyang, et al. \"TF-CLIP: Learning text-free CLIP for video-based person re-identification.\" Proceedings of the AAAI conference on artificial intelligence. Vol. 38. No. 7. 2024\n[2] Han, Qianru, et al. \"CLIP-SCGI: Synthesized Caption-Guided Inversion for Person Re-Identification.\" arXiv preprint arXiv:2410.09382 (2024).\n[3] Li, Siyuan, Li Sun, and Qingli Li. \"Clip-reid: exploiting vision-language model for image re-identification without concrete text labels.\" Proceedings of the AAAI conference on artificial intelligence. Vol. 37. No. 1. 2023.", "limitations": "The adopted datasets are previously proposed. They may not be suitable for the proposed miss-modality setting.\n\nMore sota methods should be cited for comparison though they are not proposed for solving the miss-modality problem.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "S1: The writing of this manuscript is easy to understand and follow. Figure 2 explain the pipeline of the proposed method well. All implementation details are shown in this paper.\n\nS2: The proposed setting in this paper is interesting, and it is meaningful for practical applications. The experimental results can well validate the effectiveness of the proposed method for person ReID.\n\nS3: Through experimental results, the design of several modules (such as M-bHIPR) is helpful for this task. However, the reasons for designing these modules are not well presented in this paper.\n\n\nW1: The visual encoder is initialized by the pre-trained model, such as CLIP, which may be unfair for compared methods. In addition, the authors do not claim the adopted model type of visual encoder in CLIP in the main text.\n\nW2：The reserved features of previous training steps may influence the final performance. They may harm the performance of the model rather than being helpful for prediction.\n\nW3: Too many loss items are introduced, resulting that it is hard to balance them in the overall objective function.\n\nW4: It will be better if a comprehensive dataset can be introduced for the proposed miss-modality setting. This paper only utilizes the off-the-shelf datasets for experiments, which cannot well validate the effectiveness of the proposed method", "was_revised": true, "final_justification": "The response has solved my concetns. I keep my rating.", "rebuttal": "We deeply appreciate your constructive and insightful comments. Your support means a great deal to us, and thank you for recognizing our work！Below is our point-by-point response to all Weaknesses (W), Questions (Q), and Limitations (L).\n\n---\n**W1: The details of pre-trained model.**\n\n- _**Fairness of using pre-trained VLM**_: CLIP has been widely adopted as a pre-trained backbone in various multi-modality tasks, including multi-modality ReID, due to its strong cross-modal alignment capability. Notably, one listed state-of-the-art method IDEA [25'CVPR], also utilizes CLIP as its pre-trained model. Despite sharing the same pre-trained foundation, our Miss-ReID still achieves significantly superior performance over IDEA in multiple modality-missing scenarios, e.g., `+3.7%` mAP when RGB is missing (NT) and `+4.8%` mAP when Thermal is missing (RN). These results clearly demonstrate that the advantages of Miss-ReID stem from our framework design (text-to-vision compensation mechanism) rather than the pre-trained model itself.\n\n||Backbone|NT|RT|RN|T|N|R|Mean mAP|\n|:------------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n| IDEA|CLIP(ViT-B/16)|62.9 |71.5|58.4|43.3|27.1|39.9|50.5|\n| Miss-ReID|CLIP(ViT-B/16)|66.6|72.4|63.2|47.2|34.5|43.9|54.6|\n\n- _**Visual encoder type**_:  Regarding the visual encoder in CLIP, we use the widely adopted `ViT-B/16`, which is a standard choice in many related works and ensures consistency with common experimental setups in the field. We apologize for not explicitly stating this in the manuscript and will add this detail in the revised version to enhance clarity. To ensure reproducibility, we will release our code with well-trained models.\n\n---\n**W2: The impact of reserved features in previous training steps.**\n\nWe thank you for this valuable concern.\nThe reserved features in our memory banks serve as heterogeneous prototypes for each identity, preserving multi-modality characteristics to guide pseudo-text generation when modalities are missing.​ To address potential performance interference from stale features, we employ two solutions as follows.\n\n- _**Dynamic update mechanism**_: Our memory banks first adopt a sliding average update mechanism.\nThis dynamic update filters outdated prototypes by iteratively integrating new feature information, ensuring reliable and representative semantics are retained.​\n\n- _**Training phase separation**_: Notably, our model starts learning compensatory textual features under memory supervision only after 20 epochs.\nThis delayed supervision allows our model to first capture stable visual representations, ensuring the memory-guided learning of textual features is both effective and robust.\n\n---\n**W3/Q2: Hyper-parameter analysis ($\\lambda_1$ and $\\lambda_2$ in overall objective function Eq. 11).**\n\nWe appreciate the your concern about loss balancing.\nWe have conducted hyper-parameter analysis on the key loss weights in Eq. 11.\nTable below shows the performance differences under varying $\\lambda_1$ (controlling triplet loss) and $\\lambda_2$ (controlling memory-based contrastive constraint).\n\n| ($\\lambda_1$, $\\lambda_2$) | (0.1, 0.1) |  (0.1, 0.25) | (0.25, 0.5) | (0.5, 0.25) |\n|:-----:|:-----:|:-----:|:-----:|:-----:|\n| Mean mAP|  54.6 |  53.1 | 51.4 | 51.7\n| Mean R-1  |  55.7 |  53.8 | 52.1 | 52.4\n\nThe results indicate that excessive weighting of triplet loss ($\\lambda_1$) and memory contrast  ($\\lambda_2$) impairs discriminative power, requiring careful calibration to avoid over-constraining the model.\nOur model with ($\\lambda_1$=0.1, $\\lambda_2$=0.1)  strikes this balance and yields optimal results.\nWe will add these analyses in the revision to validate balance.\n\n---\n**W4/L1: Customized modality-incomplete dataset.**\n\nWe sincerely appreciate your constructive suggestion regarding dataset suitability.\n\nTo simulate real-world modality-missing scenarios, we randomly mask images in different modalities at specific missing rates (0%-50%) on existing datasets (RGBNT201 and RGBNT100), following some practical causes.\nFor example, RGB images are masked to mimic low-light/nighttime conditions where visual details are lost; thermal images are masked to simulate sensor failures in harsh environments.\nThis approach ensures the simulated settings closely align with actual application bottlenecks, enabling valid evaluation of our method's robustness.\n\n- _**Future work**_: While current benchmarks validate core mechanisms, we fully agree that developing a dedicated dataset for modality-incomplete scenarios will strengthen real-world relevance. As you suggested, we plan to develop a comprehensive dataset tailored for the modality-missing setting.\nOur planned benchmark will cover more modality types (e.g., event/LiDAR data, sketches), establish the unified evaluation criteria, and include diverse real-world missing patterns: **1) Environmental-caused** (e.g., fog blocking RGB, rain interfering with depth sensors); **2) Hardware-caused** (e.g., temporary failure of thermal cameras); **3) Scene-caused** (e.g., partial occlusion hiding specific modalities).\n\nWe believe it is our responsibility to contribute to this field, as it will facilitate more rigorous model validation and accelerate the practical deployment of multi-modality ReID systems.\n\n---\n**Q1: Detailed motivations behind model design.**\n\nWe appreciate this valuable feedback and elaborate on the design motivations below to better highlight the novelty.\nThe core novelty of Miss-ReID lies in improving model's discriminative power in multi-modality ReID when arbitrary modalities are missing. To this end, three modules (M-bHIPR, M-iOSM and L-dMMC) are collaboratively designed to compensate the information absence based on the following motivations.\n\n- _**Memory-based Heterogeneous Identity Prototype Representation (M-bHIPR)**_: \nTo provide supervision for aligning compensated textual features in missing modalities, we construct modality-specific memory banks that store heterogeneous prototypes for each identity, ensuring rich multi-modality cues for later compensation.\n\n- _**Modality-invariant Object Structure Modeling (M-iOSM)**_: The structural patterns encode identity-specific information that is preserved regardless of the sensory modality. Therefore, we extract modality-invariant structures via structure-aware queries, capture stable identity traits (e.g., body shape) that generalize across missing modalities.\n\n- _**Language-driven Missing Modality Completion (L-dMMC)**_: By inverting structural features into identity pseudo-word embeddings that encode identity semantics and incorporating learnable modality prompts, we generate synthetic textual descriptions. Leveraging VLMs' inherent vision-text alignment, these descriptions serve as reliable substitutes for missing visual cues, optimized via memory-based contrastive constraints to ensure consistency with visual features.\n\nWe will expand these motivations in the revision for clarity.\n\n---\n**Q3/L2: Comparisons with more sota methods.**\n\nWe sincerely apologize for the incomplete comparisons.\n\nIn the manuscript, we have compared our method with several state-of-the-art open-source models (e.g., DeMo [25'AAAI], IDEA [25'CVPR]) and demonstrated that our Miss-ReID achieves superior performance in diverse modality-missing scenarios.\n\nWhile acknowledging the excellent performances of TF-CLIP [1], CLIP-SCGI [2], and Clip-reid [3], it is important to note that they primarily focus on **single-modality ReID tasks** (video-based for [1], image-based for [2, 3]).\nIn contrast, our work specifically targets multi-modality ReID with modality missing, which requires compensating and integrating complementary information across diverse modalities.\n*These single-modality methods lack the capability to handle heterogeneous multi-modality characteristics and thus cannot effectively cope with modality-missing scenarios.* A direct comparison would be unfair as the task settings and core challenges differ fundamentally. \n\nNevertheless, we recognize the value of broader benchmarking and will incorporate additional SOTA methods (including the mentioned works) in the revised manuscript to strengthen the empirical evaluation.\n\n---\n**Q4: Our contributions.**\n\nWe appreciate your valuable feedback, and our distinct contributions can be further highlighted as follows:\n\n- _**Conceptual novelty**_: Miss-ReID is **the first framework** to enable modality-missing training and inference for multi-modality ReID. Unlike most existing methods that assume complete modalities during both stages, our approach natively supports arbitrary modality-missing inputs whether during training (e.g., partial modality data for certain identities) or inference (e.g., sensor failures in deployment). This flexibility preserves multi-modality representation power while adapting to incomplete data streams, **a key step toward practical multi-modality ReID systems**.\n\n- _**Methodological innovation**_: we pioneer using Vision-Language Models (VLMs) for dynamic textual compensation in modality-missing ReID. Instead of relying solely on image or visual feature compensation, we leverage VLMs’ inherent vision-text alignment to distill structural semantics from available modalities into semantic-aligned textual embeddings.\nThis novel **\"Text-to-Visual Compensation\"** mechanism, combined with our memory banks, structure-aware query interactions, and modality-adaptive prompts, enables robust retrieval even when critical visual cues are absent.\n**It highlights VLMs as an underexplored tool for handling incomplete multi-modality data in ReID**.\n\nWe will strengthen these points in the revision to better highlight our contributions.\n\n---\nThank you sincerely for your insights, which have significantly helped refine our work.\nWe hope our responses address all concerns adequately, and we would be grateful for your consideration in supporting our submission. Grateful for your time and expertise."}, {"review_id": "WQoKAnJq8A", "reviewer": "Reviewer_zX11", "summary": "This paper proposes Miss-ReID, a novel framework for multi-modality object re-identification (ReID) that is robust to missing modalities during both training and inference. Unlike existing methods that rely on complete multi-modal inputs, Miss-ReID introduces a three-part architecture: (1) Memory-based Heterogeneous Identity Prototype Representation (M-bHIPR) to preserve modality-specific prototypes, (2) Modality-invariant Object Structure Modeling (M-iOSM) to extract shared structural features across modalities, and (3) Language-driven Missing Modality Completion (L-dMMC), which uses Vision-Language Models (VLMs) to convert visual structure into compensatory textual features. The model dynamically constructs personalized textual prompts to compensate for absent modalities, leveraging CLIP’s cross-modal alignment. Experiments on the RGBNT201 and RGBNT100 datasets demonstrate that this work reaches a satisfying performance.", "questions": "1）The L-dMMC module uses fixed modality prompts (e.g., “[modality m] attributes”), but it does not explain how these prompts adapt to different missing modality scenarios. For instance, when the RGB modality is missing, should the prompt provide more specific semantic guidance (e.g., “color characteristics under visible light”)? It is recommended to design ablation experiments to compare the performance of fixed prompts versus dynamically generated prompts, and to elaborate on the theoretical rationale behind the prompt design.", "limitations": "Please refer to Weaknesses.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1）This paper is the first to systematically address the practical challenge of modality-missing scenarios occurring during both training and inference in multi-modality ReID. By introducing a language-driven modality completion mechanism and leveraging the semantic alignment capabilities of vision-language foundation models, the proposed method exhibits strong generalizability and adaptability, representing a significant and original contribution to the field.\n\n2）The proposed three-module architecture—M-bHIPR, M-iOSM, and L-dMMC—is conceptually coherent and functionally complementary, effectively addressing modality-specific feature preservation, modality-invariant structure modeling, and semantic compensation. The extensive experimental validation, including performance comparisons under diverse missing-modality conditions and detailed ablation studies, demonstrates both the robustness and practical applicability of the framework.\n\nWeaknesses:\n\n1）The core of the model relies on the vision-text alignment capabilities of VLMs (e.g., CLIP) to compensate for missing modalities; however, the paper does not investigate the impact of different VLMs on performance. For example, when replacing CLIP with VLMs of varying parameter scales or pretraining data (such as BLIP-2 or InstructBLIP), the semantic representation capacity of the generated textual features may differ significantly, potentially leading to instability across VLM variants. Furthermore, both the visual and textual encoders of the VLMs are kept frozen, and the adaptation is performed solely through pseudo-word generation and modality-specific prompts. This \"lightweight\" tuning strategy may not fully exploit the capabilities of different VLMs, thereby limiting the model's generalizability in diverse application scenarios.\n\n2）The model employs structure-aware querying (M-iOSM) to extract modality-invariant structural features, but it relies solely on the spatial relationships of local patches (e.g., spatial configurations of body parts) and overlooks fine-grained semantic cues such as clothing textures for pedestrians or component details for vehicles. Moreover, the inversion process from structural features to pseudo-words (L-dMMC) uses a simple MLP mapping, which lacks the capacity to explicitly model semantic attributes like “color” or “material.” As a result, the generated compensatory textual features may suffer from semantic deficiency when representing complex identities.", "was_revised": true, "final_justification": "Thank you to the authors for their response. Some of my questions have been resolved. Therefore, I will maintain my original score.", "rebuttal": "We want to express our gratitude for your valuable insights.\nYour recognition of our work is deeply valued, and we’re truly grateful for your support of this research!\nBelow is our detailed, point-by-point response to all the identified Weaknesses (W) and Questions (Q).\n\n---\n\n**W1:  Some issues about using VLMs.**\n\n_**a. CLIP vs. BLIP-2**_:\nWe appreciate your insightful suggestion regarding the impact of different VLMs.\nFollowing your advice, we evaluated various VLMs as backbones (**with only text encoders frozen during training**), and the results are presented in the table below:\n\n|Backbone| Image Encoder| Text Encoder| Params (Trainable)| Params (Total)| Mean mAP|Mean R-1|\n|:------------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n| CLIP    |  ViT-B/16  | Transformer| 89.6M   |   153.0 M   |  54.6  | 55.7|\n| BLIP-2 |  ViT-L/14  | OPT-2.7B    | 310.6M |      3.1B     |  55.4  | 56.4 |\n\nThe table shows that while BLIP-2 (ViT-L/14 + OPT-2.7B) has 3.4× trainable parameters (310.6M vs. 89.6M) and 20× total parameters compared to CLIP, its performance gains (mAP +0.8%, R-1 +0.7%) are marginal. \nThis suggests stronger VLMs don’t guarantee proportional improvements in specific task, which may due to the following two reasons.\n\n- **1)** BLIP-2’s massive scale (3.1B parameters) demands larger training data to unleash its potential, and relatively small-scale ReID datasets (e.g., RGBNT201 with 4,787 aligned RGB, NIR and TIR images) cannot provide this.\n\n- **2)** BLIP-2 excels at generative tasks (e.g., image captioning), while CLIP’s contrastive pretraining aligns better with cross-modality retrieval objectives, offering a more efficient feature space for ReID.\n\nThus, despite BLIP-2's stronger general capabilities, CLIP remains a more optimal choice for our task due to its better alignment with retrieval objectives and lower computational burden. We will elaborate on these in the revision.\n\n\n_**b. Fine-tuning strategy**_: \nWe sincerely apologize for the ambiguity in our previous description regarding the fine-tuning strategy. \nOur Miss-ReID **only freezes the textual encoder** to preserve VLMs' rich semantic knowledge **while fine-tuning the visual encoder** through memory-based contrastive learning, enabling it to learn identity-specific features aligned with textual semantics.\nTherefore, by retaining the pre-trained textual encoder’s robust semantic foundation and adapting the visual encoder to **ReID-specific cues**, this strategy balances lightweight tuning with effective exploitation of VLMs, boosting generalizability across scenarios.\nMore detailed training procedure is provided in Algorithm 1 in the submission.\n\n---\n\n**W2:  Some issues about ''Structure Modeling'' and ''Inversion Network Design''.**\n\nWe sincerely appreciate your insightful comments regarding ''Structure Modeling'' and ''Inversion Network Design'', and our detailed responses are as follows.\n\n_**a. Fine-grained cues exploration during structure modeling.**_\n\nThe M-iOSM module indeed prioritizes spatial relationships as foundational modality-invariant cues. Notably, their modality-invariant nature is critical for cross-modality compensation, as they provide a stable structural foundation that, when combined with modality prompts, enables effective supplementation of identity-specific information for missing modalities.\nAdditionally, our ablation studies have validated the significance of these structural features.\n**More importantly, we fully agree that integrating fine-grained cues will further enhance the richness of compensatory features.** In the future, as you suggested, the improvements will be as follows:\n\n- **1)** We plan to introduce **fine-grained attribute heads** (e.g., texture, material) in M-iOSM to explicitly model these cues while preserving structural invariance.\n- **2)** We plan to adopt **multi-scale patch attention**, where smaller patches focus on local details like fabric textures, complementing the existing spatial relationship modeling.\n\n_**b. MLP-based inversion network design.**_\n\nTextual inversion, as a prevalent approach in recent multi-modal research, aims to discover new pseudo-words within the word-embedding space. **Following established practices, our inversion network is implemented by a multi-layer MLP, which is a simple yet efficient design widely adopted in related works[1, 2]**.\nExisting literatures [1, 2] have demonstrated that such inverted pseudo-words effectively encapsulate both holistic visual content and intricate details, including implicit semantic attributes.\nMore importantly, we fully agree that it's necessary to further strengthen its explicit semantic modeling for representing complex identities. Accordingly, our future improvements will include:\n\n- **1)** We plan to incorporate a lightweight attribute constraint module that aligns MLP outputs with a predefined semantic dictionary (covering \"texture\", \"material,\" etc.) via contrastive loss;\n\n- **2)** We plan to add a cross-attention layer between structural features and attribute embeddings to enhance the inversion network’s ability to map fine-grained visual cues to corresponding textual semantics.\n\nThese improvements will address the semantic deficiency concern while maintaining the model’s efficiency. We will elaborate on these in the revision.\n\n**References**:\n\n[1] \"An image is worth one word: Personalizing text-to-image generation using textual inversion.\"  In International Conference on Learning Representations (ICLR), 2022.\n\n[2] \"A pedestrian is worth one prompt: Towards language guidance person re-identification.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\n---\n\n**Q1:  Modality prompt variants.**\n\nWe sincerely apologize for the confusion regarding the design of modality prompt.\nIn fact, the L-dMMC module does not use fixed modality prompts but instead introduces **learnable modality prompts** tailored to each modality.\nThese prompts encode modality-specific information for missing modalities and, combined with the inverted identity pseudo-word, construct personalized linguistic description for each unavailable modality.\nThey are dynamically updated through memory-guided optimization, enabling adaptive adjustments to diverse missing scenarios. To validate the effectiveness of our prompt design, we conducted ablation experiments on modality prompt variants, as shown Table below.\n\n|Variants|Mean mAP|Mean R-1|\n|:------------|:-----:|:-----:|\n| T-A (*w/o* Modality Prompt) |     53.9 |   54.8   |\n| T-B (Fixed Modality Prompt) |     54.0 |  55.0   |\n| T-C (Learnable Modality Prompt) | 54.6  | 55.7|\n\n- **T-A** ('_An image of a [Inverted Pseudo-word] person._') omits modality prompts entirely, relying only on identity pseudo-words;\n\n- **T-B** ('_An image of a [Inverted Pseudo-word] person in visible/near infrared/thermal infrared spectrum._') uses fixed descriptive phrases (e.g., \"in visible spectrum\") tied to modalities;\n\n- **T-C** ('_An image of a [Inverted Pseudo-word] person, who shows the [Learnable Modality Prompt] attributes._') employs learnable prompts that adaptively capture modality-specific semantics.\n\nThe performance of T-A shows modality prompts are critical for cross-modal alignment. The weak improvement by-B confirms modality-specific guidance helps, but rigid templates lack contextual awareness. T-C outperforms T-A and T-B, confirming learnable prompts’ superiority.\n\n**The rationale behind learnable modality prompts is threefold**: 1) They dynamically adapt to pre-trained VLMs’ semantic space, aligning better with VLM’s inherent knowledge than rigid fixed phrases.\n2) They adaptively encode modality-specific nuances (e.g., RGB’s color cues, thermal’s temperature features) that fixed prompts fail to capture, enhancing compensation precision for missing modalities.\n3) Combined with inverted identity pseudo-words, they achieve dual personalization (modality + identity), strengthening fine-grained identity discrimination critical for ReID.\n\nWe will elaborate on these details and add more analysis in the revision.\n\n---\n\nSincere thanks for your valuable insights, which have been instrumental in enhancing our work. We hope our replies fully address the raised issues, and we’d be honored by your backing of our submission. Appreciate your time and expertise."}, {"review_id": "DpV3GuOgpf", "reviewer": "Reviewer_uuXE", "summary": "The paper presents Miss-ReID, a flexible framework for robust multi-modality object re-identification (ReID) under modality-missing conditions. It is the first to support both modality-missing training and inference, leveraging Vision-Language foundation Models (VLMs) for vision-text knowledge transfer to compensate for missing visual cues. The framework comprises three modules: Memory-based Heterogeneous Identity Prototype Representation (M-bHIPR) to store modality-specific prototypes, Modality-invariant Object Structure Modeling (M-iOSM) to distill structural features, and Language-driven Missing Modality Completion (L-dMMC) to generate compensatory textual embeddings via inverted pseudo-word tokens and modality prompts. Extensive experiments on RGBNT201 and RGBNT100 show that Miss-ReID outperforms state-of-the-art methods in various modality-missing scenarios, demonstrating the lowest performance declines and advancing multi-modality ReID for real-world applications.", "questions": "1. The framework leverages VLMs for modality compensation, which might incur higher computational costs. Could the authors provide inference time comparisons between Miss-ReID and state-of-the-art methods under modality-missing scenarios to clarify its practical efficiency?\n2. The M-bHIPR module maintains modality-specific memory banks, but when the modality missing rate is high, does the prototype update strategy in Eq. (3) ensure effective preservation of identity characteristics? Are there additional experiments to verify its robustness under low-sample conditions?", "limitations": "Yes. The authors have explicitly discussed both technical limitations further works are required to improve resilience in extreme cases (e.g., complete modality collapse) and expanded modality integration (e.g., event/LiDAR data, sketches, audio).", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. It is the first work to handle multi-modality ReID under general modality-missing scenarios during both training and inference, enabling arbitrary modality-missing inputs and promoting real-world deployment of multi-modality ReID.\n2. By leveraging the inherent vision-text reasoning capabilities of Vision-Language foundation Models (VLMs), it dynamically compensates for missing visual cues through semantic-aligned textual embeddings, showing great potential in addressing incomplete data streams in Multi-modality ReID.\n3. The framework consists of three collaborative modules (M-bHIPR, M-iOSM, and L-dMMC), which work together to extract features, model structures, and complete missing modalities, ensuring the preservation of multi-modality characteristics and effective compensation for information absence.\n4. Extensive experiments on benchmark datasets demonstrate its superiority over state-of-the-art methods in various modality-missing scenarios, with the lowest performance declines in mAP and Rank-1 accuracy, verifying its efficacy and robustness.\n\nWeaknesses:\n\n1. It needs further improvement in resilience for extreme cases like complete modality collapse, which limits its performance in some harsh conditions.\n2. The integration of VLMs may lead to higher computational requirements, potentially posing challenges for low-resource or real-time deployment scenarios, there is a certain increase in parameters and FLOPs.", "was_revised": true, "final_justification": "I am pleased to raise my rating to 5, and support acceptance of this paper.", "rebuttal": "We are truly grateful for your constructive and insightful feedback. Your recognition of our work means a great deal, and we deeply appreciate your support. Below is our detailed, point-by-point response to all the identified Questions (Q) and Weaknesses (W).\n\n---\n\n**Q1/W2: Model complexity and inference speed.**\n\nWe appreciate your valuable concerns about computational complexity and inference speed, which are critical for real-world deployment. As suggested, we provide detailed comparisons about model complexity and inference speed between Miss-ReID and SOTA methods.\nThe table below reports key metrics: trainable parameters, FLOPs, and FPS across both modality-complete and modality-missing scenarios. All evaluations were conducted on an NVIDIA RTX A6000 GPU.\n\n|Methods|Backbone|Params (Trainable)| FLOPs|FPS (No Miss)|FPS (1 Miss)|FPS (2 Miss)|Mean mAP|\n|:------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n| DeMo (25'AAAI)   |CLIP(ViT-B/16)  |98.8 M| 35.1 G| 28.7 | 47.7| 97.8 |50.7|\n| IDEA (25'CVPR)  |CLIP(ViT-B/16)  |91.7 M|43.6 G|  15.9 | 24.1  | 46.3 |50.5|\n| Miss-ReID (Ours) |CLIP(ViT-B/16)  |89.6 M|43.6 G|37.5 | 34.9 | 35.1   |54.6|\n\n- _**Inference speed**_:  Our Miss-ReID strikes **a favorable balance between performance and inference speed** across both modality-complete and -missing scenarios. Specifically, it outperforms SOTA methods (DeMo, IDEA) in Mean mAP while meeting real-time requirements (>30 FPS), making it suitable for practical systems like surveillance.\nIn modality-complete setting, our model achieves 37.5 FPS, significantly exceeding IDEA (15.9 FPS) and DeMo (28.7 FPS). This efficiency advantage thanks to its selective VLM activation only for missing modalities, balancing performance and speed.  In modality-missing scenarios (especially with 2 modalities missing), Miss-ReID maintains stable real-time speed (35.1 FPS) while achieving the best mAP. In contrast, DeMo and IDEA fail to match our performance despite higher speeds.\n\n- _**Computational costs**_:  While Miss-ReID utilizes similar training FLOPs (43.6 G) to IDEA (CVPR'25), our model has fewer trainable parameters (89.6 M) than others (91.7 M and 98.8 M), thereby reducing training memory overhead. \n\n- _**Lightweight variant**_: We plan to develop a lightweight variant of Miss-ReID for edge devices, focusing on three optimizations: 1) Prompt Distillation via KL divergence to compress text encoder layers (50% reduction); 2) Structured Pruning to remove redundant attention heads (20% fewer parameters); 3) Replacing ViT-B with MobileViT-XS, slashing backbone FLOPs by 60%. These aim to deliver edge-ready performance while preserving core compensation capabilities.\n\nWe will add these details in the revision to clarify practical efficiency.\n\n---\n\n**Q2/W1: Robustness in some harsh conditions.**\n\nWe appreciate these critical observations on robustness under harsh conditions. Regarding extreme modality loss (e.g., high missing rates), we supplement experimental results under varying tri-modality missing rates ($ \\eta = (\\eta_{rgb}, \\eta_{nir}, \\eta_{tir}) $, representing the proportion of missing samples per modality), as reported in Table below.\n\n| Missing Rates|$ \\eta $ =  (0.5, 0.5, 0.5) | $ \\eta $ =  (0.7, 0.7, 0.7) | $ \\eta $ =  (0.9, 0.9, 0.9) | \n|:------------:|:-----:|:-----:|:-----:|\n| Mean mAP |  47.3  |  38.6 | 12.9|\n| Mean R-1   |  47.9  |  36.7 | 9.7|\n\nThese results indicate that, under moderate high rates, _i.e._, $ \\eta $ =  (0.7, 0.7, 0.7), our model retains reasonable performance (mAP=38.6), demonstrating resilience by leveraging memory banks and textual compensation.\nAs expected, at extreme rates, _i.e._, $ \\eta $ =  (0.9, 0.9, 0.9), performance drops significantly, as limited remaining samples weaken prototype reliability in M-bHIPR and reduce structural cues for L-dMMC, which is consistent with the challenge of near-complete modality collapse.\n\nThis decline can be attributed to two factors. **First**, the M-bHIPR module’s prototype update (Eq. 3), which currently averages features from available samples, struggles when data is scarce, _e.g._, $ \\eta $ =  (0.9, 0.9, 0.9).\n**Second**, insufficient training data undermines the model’s ability to learn robust features.\nThis highlights the need to enhance model robustness under low-sample conditions.\n\n**Our future improvements will include**: **1)** Dynamically weighting prototypes in M-bHIPR (_e.g._, prioritizing high-confidence samples and using knowledge distillation to retain historical reliable prototypes); \n**2)** Strengthening L-dMMC with cross-modality attention to extract richer semantics from sparse remaining cues;\n**3)** Adding adversarial training for extreme cases, _e.g._, $ \\eta $ =  (0.9, 0.9, 0.9), to boost model generalization.\n\nWe will include these analyses and plans in the revision, along with comparisons to adapted baselines under high missing rates.\n\n---\n\nThank you sincerely for your valuable insights, which have greatly refined our work. We hope our responses address the raised concerns thoroughly, and we’d be honored by your support for our submission. Thank you for your time and expertise."}]

Author rebuttal:

Meta-review: The paper presents Miss-ReID, a framework for multi-modality object re-identification under missing modality conditions during both training and inference, using vision-language models to compensate for absent visual inputs.

Reviewers thought the paper addresses a realistic and underexplored problem setting, presents a technically coherent and modular architecture, and demonstrated strong empirical results. They also thought the paper was well written and had thorough ablation studies.Concerns raised in the initial reviews included lack of clarity in the novelty claim versus prior work, limited exploration of VLM variants, and architectural details around structure modeling and inversion mechanisms. However, most of these were effectively addressed in the rebuttal and discussion, and reviewers seemed satisfied.

Given the sound technical contributions, practical significance, and resolution of key concerns during the discussion, as well as the final ratings of accept and three borderline accepts, the AC recommends acceptance.
Decision: Accept (poster)