Domain Adaptive Hashing Retrieval via VLM Assisted Pseudo-Labeling and Dual Space Adaptation
Authors: Jingyao Li
Zhanshan Li
Shuai Lü
Venue: NeurIPS 2025 poster ()
Keywords: Domain Adaptive Hashing
Image Retrieval
Pseudo-Labeling Strategy
Unsupervised Domain Adaptation
Primary area: deep_learning
TL;DR: We propose a VLM-assisted dual-space adaptation framework to improve unsupervised domain adaptive hashing by calibrating pseudo-labels and decoupling feature and Hamming space alignment.

Abstract: Unsupervised domain adaptive hashing has emerged as a promising approach for efficient and memory-friendly cross-domain retrieval. It leverages the model learned on labeled source domains to generate compact binary codes for unlabeled target domain samples, ensuring that semantically similar samples are mapped to nearby points in the Hamming space. Existing methods typically apply domain adaptation techniques to the feature space or the Hamming space, especially pseudo-labeling and feature alignment. However, the inherent noise of pseudo-labels and the insufficient exploration of complementary knowledge across spaces hinder the ability of the adapted model. To address these challenges, we propose a Vision-language model assisted Pseudo-labeling and Dual Space adaptation (VPDS) method. Motivated by the strong zero-shot generalization capabilities of pre-trained vision-language models (VLMs), VPDS leverages VLMs to calibrate pseudo-labels, thereby mitigating pseudo-label bias. Furthermore, to simultaneously utilize the semantic richness of high-dimensional feature space and preserve discriminative efficiency of low-dimensional Hamming space, we introduce a dual space adaptation approach that performs independent alignment within each space. Extensive experiments on three benchmark datasets demonstrate that VPDS consistently outperforms existing methods in both cross-domain and single-domain retrieval tasks, highlighting its effectiveness and superiority.

Domain Adaptive Hashing Retrieval via VLM Assisted Pseudo-Labeling and Dual Space Adaptation
Jingyao Li Zhanshan Li Shuai Lü
CCollege of Computer Science and Technology, Jilin University, Changchun 130012, China "Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012, China
Email addresses: jingyao edu.cn, cn, lizs@jlu. wet.jjl..euu...m edu. lus@jlu. edu.. edu. cn
Abstract
Unsupervised domain adaptive hashing has emerged as promising approach for efficient and memory-friendl cross-domain retrieval. It leverages the model learned on labeled source domains to generate compact binary codes for unlabeled target domain samples, ensuring that semantically similar samples are mapped to nearby points in the Hamming space. Existing methods typically apply do- main adaptation techniques to the feature space or the Hamming space, especially pscudo-labeling and feature alignment. However, the inherent noise of pseudo- labels and the insufficient exploration of complementary knowledge across spaces hinder the ability of the adapted model. To address these challenges, pro- pose a Vision- languae model assisted Pseudo labelig and Dual Space adaptation (VPDS) method. Motivated by the strong zero-shot generalization capabilities of pre-trained vision-language models (VLMs), VPDS leverages VLMs to calibrate pseudo-labels, thereby mitigating pseudo-label bias. Furthermore, simultane- ously utilize the semantic richness of high-dimensional feature space and preserve discriminative efficiency of oi-ddimnsioonll Hamming space, we introduce a dual space adaptation approach that performs independent alignment within each space. Extensive experiments on three benchmark datasets demonstrate that VPDS con- sistently outperforms existing methods in both cross-domain and single-domain retrieval tasks, highlighting its effectiveness and superiority.
Introduction
Hashing retrieval aims to map samples into compact low-dimensional binary codes, enabling fast and memory- memory-efficiet retrieval via lightweight bit-wise operations in the Hamming space [12. 52 Existing hashing methods are typically categorized into supervised and unsupervised benefit from rich label information and enable the learning of highly discriminative representations, but they require large-scale labeled training data and assume that training and query data are drawn from the same distribution, which are difficult to be satisfied real-world applications. Unsupervised hashing methods (24, 26 46) eliminate the dependency labeled training data, but their performance is limited by the absence of reliable supervision and they still struggle with the distribution shift between training and query data.
53. 57 65
approaches. Supervised hashing methods [38 59, 63)
Unsupervised domain adaptation (UDA) 22.5 10. 34, 44. 45, 60) is developed to transfer knowledge from the labeled source domain to the unlabeled target domain, releasing the need for distribution consistency across domains. This makes UDA compelling solution for addressing the challenges in both supervised and unsupervised hashing. As result, domain adaptive hashing retrieval [3] 14
Correspooning author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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49 50,00 51
54] has gained increasing attention. The goal of domain adaptive hashing methods is to reduce the distribution discrepancy between the training and query data, while maintaining semantic structure within the Hamming space to ensure effective cross-domain retrieval.
To reduce distribution discrepancy across domains, most existing methods perform distribution align- ment either in the high-dimensional feature space or in the low-dimensional Hamming space. Com- mon strategies include minimizing the distribution difference measured by statistical metries [14 48] or adversarial training with a discriminator (29, 49 However, features in the feature space possess richer semantic expressiveness than hash codes in the Hamming space, which are constrained by their dimensionality. When alignment is performed solely in the feature space, the aligned feature structure can be distorted when mapped into the Hamming space due inevitable information loss. Conversely, when alignment is performed solely in the Hamming space, due to limited representa- tional capacity, semantically similar samples can be misaligned even if global distributions are well aligned. Recent works have attempted to jointly align the feature and Hamming spaces, for example, via concatenated feature- hash representations [49] or structural consistency constraints F. Despite showing some effectiveness, such methods enforce tight coupling between the feature and Ham- ming spaces. Given the substantial disparity in their dimensionality and semantic capacity, we argue that this tight coupling restricts alignment flexibility. Instead, propose decoupled alignment strategy that performs domain alignment independently in the feature and Hamming spaces, without imposing strict cross cross-space consistency. This relaxed design enables more flexible integration of complementary information from each space, leading to improved adaptation performance.
To maintain semantic structure in the Hamming space, pseudo-labeling is one of the most commonly used approaches. In the absence of ground-truth labels in the target domain, the generated pseudo- labels inevitably contain noise, which can misguide the adaptation process. To alleviate this issue, prior works have introduced various strategies, such as confidence- based thresholding 11 43, 61]. auxiliary modules to reduce the bias to the source domain 128 35 50, 581 and uncertainty-baase (64) While these ap- proaches alleviate pseudo-label noise, they remain susceptible to the bias introduced by the domain gap, especially in the early stages of training. With the recent emergence of pre-trained vision- language models (VLMs) 117 36) that exhibit strong zero-shot generalization capabilities, motivated to leverage VLMs to calibrate pseudo-labels, thereby enhancing the reliability of pseudo- labels and providing more accurate supervision for target domain adaptation.
weighting schemes to modulate the influence of noisy pseudo-labels [49
In this paper, we propose a VLM assisted Pseudo-labeling and Dual Space adaptation (VPDS) method. Considering that the high-dimensional feature space provides richer semantic information, while the low-dimensional Hamming space is essential for efficient and discriminative retrieval, VPDS performs pseudo-labeling in the feature space and utilizes the generated pseudo-labels to guide the learning of compact and discriminative hashing codes. To enhance the reliability of pseudo-labele VPDS leverages the strong generalization ability of VLMs to provide calibration information. Moreover, VPDS achieves cross-domain alignment by reducing the domain gap in the feature space and Hamming space separately, facilitating the utilization of space-specifie knowledge and enhancing overall adaptation performance.
The contributions of this paper are summarized as follows: (1) We propose VPDS that decouples cross-domain alignment into independent alignments in the feature space and Hamming space, ex- ploring the complementary information across spaces. (2) We propose a VLM assisted pseudo- labeling strategy to enhance pseudo-label reliability. And this strategy is model-agnostic and can be seamlessly incorporated into other unsupervised learning frameworks. (3) Experiments three standard benchmark datasets demonstrate that VPDS achieves superior performance in both cross- domain and single-domain retrieval scenarios.
2 Related Works
Unsupervised Domain Adaptation. To achieve knowledge transfer from the labeled source domain to the unlabeled target domain, UDA methods aim to learn a domain domain-invarian invariant feature space. Early approaches rely on statistical metrics, such as Maximum Mean Discrepancy (MMD) [30, 32), CORe-
lation ALignment (CORAL) [4] 42 and Maximum Density Divergence (MDD) (22). to quantify
the cross-domain distribution discrepancy, which are then minimized to mitigate the domain gap.
With the advent of generative adversarial networks, adversarial UDA methods 7 31. 55 56 in-

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troduce a domain discriminator to measure the separability of the two domains. Through training the feature extractor and the discriminator in min-max manner, Nomain-invarrinn features can be obtained. More recently, pre-trained VI,,s, especially the contrastive language-image pre-training (CLIP) (36 model, have been incorporated into UDA frameworks. These methods typically freeze the backbone parameters of CLIP and design auxiliary adaptation modules [8 20 40). For in- stance, DAMP [6] introduces a mutual prompting module to learn domain-agnostic prompts and domain-invariant visual embeddings. UniMoS [23] employs modality separation strategy to en- hance Iision-languag interplay. While UDA has shown impressive progress in tasks such as image classification and semantic segmentation, its application to image retrieval remains relatively under- explored. In this work, we focus on advancing UDA techniques for image retrieval.
Domain Adaptive Hashing, Similar to UDA, domain adaptive hashing also needs to reduce the domain gap. To achieve this, domain adaptive hashing methods typically employ alignment strate- gies based on statistical metrics [14, 488 62) or adversarial training (29, [49]. Domain adaptive hashing first encodes data into a nigh-dimensional feature space and subsequently maps it to low- dimensional Hamming space. Consequently, alignment is typically performed either in the feature space (14. 29 48 54] or in the Hamming space [50, 51]. As the feature space can provide rich semantic information for knowledge transfer, and the Hamming space is crucial for efficient and discriminative retrieval, some recent approaches attempt to exploit both and achieve joint alignment of the two spaces. For example, PEACE [49] concatenates features with their corresponding hash codes, and alignment is performed on the concatenated features. CPH [3] introduces a hypersphere space for feature alignment, then the aligned feature structure is preserved in the Hamming space. However, these joint alignment strategies tightly couple the two spaces, potentially restricting adap- tation performance. To this end, we propose perform alignment separately within each space, allowing the model to exploit the distinct characteristics of each space more effectively.
Pseudo-Labeling Strategy. Pseudo-labeling is widely adopted strategy for extracting discrimi- native information from the unlabeled target domain. Due to the absence of ground-trutt labels, pseudo-label bias is inevitable. To mitigate this bias, some methods use threshold to select high- confidence labudo-labels [1] 43, [1]. Some methods design an auxiliary module, such as a teacher network [35 5d 58) or a target domain-specine
model [28). to reduce the impact of source domain information. The topological structure of the feature space provides an alternative strategy for gen- erating pseudo-labels, allowing the model and class prototypes to be iteratively optimized and thus mutually reinforcing [1. 3 [54]. Additionally, some works measure the pseudo-label uncertainty and incorporate it as weighting in the learning objective (49. 64]. Despite these efforts, most methods rely solely on the adapted model, which is affected by the domain gap especially in the early stages of training. To thie end, we propose leveraging pre-trained VLMs provide external calibration for enhancing pseudo-label reliability. Our method mitigates early-stage pseudo-label bias and prevents its accumulation throughout training, resulting in more stable and effective adaptation.
3 Method
The source domain containing N₂ labeled samples is denoted as D = {xx 8 "i and the target domain containing N, unlabeled samples is denoted as D' = [ i=1- Assuming that D and D' follow different data distributions but share the same label space D. The objective of domain adaptive retrieval is to learn a model on D* and D', which can map a samplex x E UD' to a binary code b E [-1,1]%, where L represents the code length. The learned model should ensure that semantically similar samples are mapped to compact binary codes in the Hamming space, enabling effective retrieval in both cross-domain and single-domain retrieval tasks. In cross-domain retrieval, query and retrieval samples are from D' and D=, respectively. In single-domain retrieval, both query and retrieval samples are from D'. The framework of our method is shown in Figure II
3.1 Dual Space Adaptation
Feature space and Hamming space are two commonly used spaces where cross-domain alignment is performed. The high- nigh-dimensional feature space provides rich semantic capacity, making effective for capturing transferable knowledge across domains. In contrast, the low-dimensional Hamming space crucial for efficient and discriminative retrieval via compact binary codes. Although exist- ing approaches have attempted to leverage the complementary strengths of both spaces, they often
3

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Source domain
Feature space
Hamming space
Feature Encoder F
Hash Encoder
Target domaii
Classifier C2
Classificr C
VLM
Image Encoder F,
Weighted Integration
4
"A photo of alans clock."
Text Encoder Fr
"A photo of bike."
Memory hank M
Memory hank M
"A photo lamp."
Leamaale
Frozen
Source domain dataflow
Tarpol doraain ditaflow
colculation datafllw
Memory bank update
Memory bank
Memory bank M
Figure 1: Overview of the VPDS framework. Pseudo-labels generated in the feature space and used to guide the learning of discriminative hash codes. A frozen pre-trained VLM is utilized to provide auxiliary corrective information for improving pseudo-label reliability. Classifiers in the feature and Hamming spaces preserve the discriminability of learned representations, while memory bank-based domain alignment within each space facilitates capturing transferable knowledge.
impose strict structural consistency constraints between them. However, the dimensionality reduc- tion from feature space to Hamming space inevitably induces information loss, which can distort semantic relationships and alter the space structure. Therefore, we argue that enforcing strict struc- tural consistency between the two spaces suppress the leverage of advantages unique to each space, thereby constraining the overall capacity for effective knowledge transfer. Based on this idea, we propose dual space adaptation strategy that performs domain alignment independently within each space, while relaxing mutual constraints across spaces to preserve their respective strengths.
Both transferability and discriminability are essential for effective model adaptation. The discrim- inability usually comes from two sources, including the ground-truth labels in the source domain and the self-supervised information in the target domain. Therefore, the model is initially trained on labeled source domain and subsequently used to generate pseudo-labels for the unlabeled target domain, thereby enabling self-supervise learning within the target domain. Since our dual space adaptation strategy performs cross-domain alignment in two spaces separately, we introduce two classifiers: CF in the feature space and Ch in the Hamming space, to ensure that sufficient discrim- inative information is retained in each space. These two classifiers are learned on source domain features f and hash codes b", respectively, and the supervised loss function is formulated
N, C. = N Σ CE(Cr(f)). vf) + (B(CH(b))
where of is the ground-truth label of x; CE(-) represents the cross-entropy loss, Cr(.) and CH(() indicate the prediction results.
For the unlabeled target domain, we leverage the feature space to generate pseudo-labels, as it con- tains richer semantic information, providing sufficient knowledge foundation for capturing dis- criminative information. The generated pseudo-labels are then used to supervise the learning of

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discriminative hash codes in the Hamming space. Although this process introduces connection between the two spaces, the information in the Hamming space does inversely affect the feature space. Thus the mutual constraints between the two spaces of prior works are relaxed to unidirec- tional constraint. Formally, for the target domain feature f", the pseudo-label can be obtained by the adapted model, namely, 8² = Cr.(f). This paper proposes via VLM, which will be introduced in Section 3.2 and the enhanced pseudo-label is denoted as y². Then, y' is used for the self-supervised learning in the Hamming space:
ppeudo-labeling strategy to enhance j'
N CE(CH (b)).
L
In addition, we use the information maximization technique 6 mine semantic structure information of the unlabeled target domain. In each space, the information maximization objective can be formalized as:
13 25 in each space to further
N. DI of
DI ΣE
C, 55ΣNS
P
F logF"
where jff denotes the confidence of Cr predicting the feature fy the e-th class, Ch predicting the hash code by as the e-th class. F = ºे Σ ff represents the average class confidence over all target samples. The first term represents the entropy of the predictions, encouraging confident and discriminative classification, while the second term promotes prediction diversity across classes, mitigating the risk of class collapse.
To enhance transferability, we propose a prototype-based global alignment strategy. Unlike prior works that perform class-level alignment using class prototypes, we treat prototypes the most representative samples and align domain-level distributions. Given that the model is continuously updated during training, the constructed prototypes may deviate from the true class centers. There- fore, rather than enforcing striet class- level alignment, we focus on global distribution alignment, while the class-level alignment can be implemented via classifiers CF and Ch. In the feature space. we construct two memory banks to store class prototypes of the source and target domains, denoted as M E R Mixdr and M E R xdr, respectively, where dF is the dimension of features in the feature space. To improve training stability and robustness, we adopt momentum-based strategy to incrementally update the memory banks at each training iteration:
M pe aM, + (1-a)Prr Per M aM'r p. + (1-a)) Pers'
(4)
where e denotes the iteration number, a is a momentum coefficient controlling the update rate. P and P represent the class prototypes of the source and target domains, respectively, computed by the features in the feature space. These prototypes are formulated as:
P
F
Σ ft. #TEB:
P F.
ES
B
B
where B& denotes the set of source domain samples with ground-truth label c, and By, denotes the set of target domain samples with pseudo-label c. Then the domain-level alignment in the feature space achieved by aligning each source domain prototype with each target domain prototype:
Lp,
EM M
(6)
Similar the feature space, we construct two memory banks in the Hamming space, denoted as M; E RID and M f E RR) where dæ is the length of hash codes in the Hamming space. The update strategy for M H and M и follows the same momentum-based rule as described in Eq. 44) And the class prototypes P; i and P' in the Hamming space are computed by the hash codes b* and b", following the same strategy as in Eq. (5). Then the domain-level alignment in the Hamming space achieved via:
Lr
DI MM; x M
As result, the dual space alignment approach can be implemented by minimizing:
Lp Lrp + Lrg-
(8)
5

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3.2 VLM Assisted Pseudo-Labeling
In the previous section, pseudo-labels are utilized in Eqs. and ( thus their reliability plays critical role in the udaptation process. Most existing methods rely solely on the adapted model to generate pseudo-labels which can lead to pseudo-label bias due to the domain gap. To address this challenge, we are motivated by the strong generalization capabilities of the pre-trained VLMs, and propose to incorporate VL.Ms as auxiliary guidance for pseudo- generation, partieularly in the early stages of training when the model has not yet adapted to the target domain. To be specific, the final pseudo-labels are the combination of predictions from the VLM and from the adapted model, where the relative contributions of two terms are dynamically adjusted throughout training, As train- ing progresses, the model progressively learns more target domain- specific knowledge, resulting in increasingly reliable predictions. Accordingly, we gradually increase the contribution of the adapted model, while gradually reducing the reliance on the VLM.
In this paper, we use CLIP the VLM framework. To obtain predictions based on CLIP, the textual label descriptions are formalized as Te "a photo of a [C'LASS(]". Based on the similarity image features and textual features, which are encoded by the image encoder F, and text encoder Fr of CLIP, the prediction result can be obtained through:
Velip Cosine(PFT(T)) Fr(x"))
(9)
Based on jf and Yrdips we construct the final pseudo-label by:
arg m + -1- B)yoipp
(10)
here, B is implemented as a linearly increasing parameter, namely == e/E, where e denotes the current training iteration and E is the total number of training iterations. Since CLIP is not optimized in method, Yelip can be computed once in the first epoch and stored for use in subsequent epochs. Therefore, this pseudo-labeling strategy incurs minimal computational overhead, and can be seamlessly incorporated with other unsupervised learning frameworks.
3.3 Training and Inference
Finally, the overall training objective of our method is formalized as:
C == L. + n/e, + + + Lr,
(11)
where 7) and y are the trade-off parameters. Since the sign(-) function used to generate binary hash codes is non-differentiable and thus unsuitable for gradient-based optimization [4)] we adopt its smooth approximation tanh(-) during training. Specifically, we use b = tanh (F(())) for training, and use b (ign(H(((())) for inference, where F(-) and (-) denote the feature encoder and hash encoder, respectively.
Experiment
4.1 Setup
Datasets. We evaluate our method on three benchmark datasets. Office-Home [48] consists domains from 65 classes. Consistent with prior works ( 51]5 we construct 6 tasks based this dataset. Office-31 [37] contains 3 domains, with each domain containing 31 classes. Digits consists of 2 domains, MNIST 21 and USPS [16], and each domain contains 10 handwritten digits.
Baselines. We compare our method with several state-of-the art methods, including unsupervised
hashing methods (i.e., ITQ [9] DSH [19,, SGH [18] OCH (27) and GraphBit 531) and domain
adaptive hashing methods (i.c., CTH-g (62). PWCF [15]. DAPH [4]. DHLing [54]. PEACE (49),
DANCE (50). IDEA (51). CPH
3] and COUPLE (33).
Implementation Details. For both cross-domain and single-domain retrieval tasks, 10% of the target domain samples are randomly selected as the query set. The remaining 90% along with all source domain samples constitute the training set. The retrieval set is constructed from the training set, depending on whether the task is cross-domain or single-domain retrieval. The feature encoder F is implemented using the VGG (39] backbone. For the VLM,, we adopt the pre-trained CL.PP (36) with ViT-B/16 [4] and keep its parameters frozen during training. The model is optimized using

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Table 1: MAP (%) of cross- drosi-domain retrieval tasks with 64-bit hash codes on Office-Home and Office-31. The best and second-best results are highlighted in bold and underlined.
Office-Hoone Home
Office-31
Task
A-+R
R-A
C-R
R-C
P-R
R-P
A-D
A-
D-+A
D-W
W-+
W-D
Avg
ITQ
25.88
25.37
14.83
14.92
26.81
28.19
29.55
28.53
26.83
58.89
25.09
58.00
30.24
DSH
9.69
9.67
5.47
5.28
8.49
8.26
16.66
15.09
16.33
41.07
13.58
39.24
15.74
SGH
22.93
22.53
13.62
13.51
24.51
25.73
24.98
22.47
22.17
56.36
20.52
53.94
26.94
OCH
18.09
17.54
10.27
10.05
18.65
20.15
24.86
22.49
22.45
53.64
20.79
51.03
24.17
GraphBit
18.18
16.87
11.51
10.81
18.91
21.32
24.48
23.12
22.09
53.82
21.34
51.43
24.49
G
16.95
17.54
11.88
17.82
18.57
30.85
18.44
21.99
48.48
20.02
50.23
23.56
PWCF
34.57
28.95
24.22
18.42
34.03
34.44
39.78
34.86
35.12
72.91
35.01
67.94
38.35
DAPH
21.19
22.28
13.25
12.26
26.61
24.26
29.60
22.94
25.48
60.67
24.31
45.42
31.85
DHLing
48.47
30.81
38.68
45.24
25.15
43.30
41.96
45.10
75.23
42.89
41.74
79.91
46.54
PEACE
45.97
42.68
38.72
28.36
53.04
54.39
46.69
48.89
46.91
83.18
46.95
78.82
51.22
DANCE
44.53
43.54
39.03
28.87
53.73
55.14
44.78
47.66
46.68
84.75
48.61
78.39
51.31
IDEA
51.19
49.64
45.71
32.77
59.18
61.84
48.70
54.43
53.53
88.69
53.71
84.97
57.03
COUPLE
54.14
54.35
49.24
41.39
63,94
64.29
50.27
59.32
56.04
88.90
56.35
85.26
60.29
CPH
71.18
63.28
58.65
42.84
71.27
74.77
68.37
60.61
52.84
95.88
60.14
99.90
68.31
VPDS
72.54
63.36
63.58
41.95
75.17
81.80
87.22
91.40
65.88
98.91
64.88
100
75.56
Table 2: MAP (%) of cross-domain retrieval tasks with various bits Digits. The best and second- best results are highlighted in bold and underlined.
MNIST-USH
USPS -NIST
Bit
16
32
48
64
96
128
16
32
48
64
96
128
Avg
ITQ
13.05
15.57
18.54
20.12
23.12
23.89
13.69
17.51
20.40
20.30
22.79
24.59
19.46
DSH
20.60
22.21
23.68
24.28
25.73
26.50
19.54
21.22
22.89
23.79
25.91
26.46
23.57
SGH
14.24
16.69
18.72
19.70
21.00
21.95
13.26
17.71
18.22
19.01
21.69
22.09
18.69
OCH
13.73
17.22
19.59
20.18
20.66
23.34
15.51
17.75
18.97
21.50
21.27
23.68
19.45
GTH-g
20.45
17.64
16.60
17.25
17.26
17.06
15.17
14.07
15.02
15.01
14.80
17.34
16.47
PWCF
47.47
51.99
51.44
51.75
50.89
59.35
47.14
50.86
$2.06
52.18
57.14
58.96
52.60
DAPH
25.13
27.10
26.10
28.51
30.53
30.70
26.60
26.43
27.27
27.99
30.19
31.40
28.16
DHLing
49.24
54.90
56.30
58.28
58.80
59.14
50.14
51.35
53.67
58.65
58.42
59.17
55.67
PEACE
52.87
59.72
60.69
62.84
65.13
68.16
53.97
54.82
58.69
60.91
62.65
65.70
60.51
DANCE
53.18
57.98
61.23
63.15
65.92
68.87
54.31
55.64
57.26
61.49
63.43
66.23
60.72
IDEA
58.89
64.48
65.72
67.48
70.24
74.34
60.99
61.47
65.45
67.97
69.72
72.31
66.59
CPH
64.55
63.33
65.94
71.04
68.06
71.85
54.36
61.64
64.17
65.59
68.47
70.70
65.81
COUPLE
60.56
66.05
66.23
67.98
73.02
75.12
63.28
64.94
67.44
70.19
72.87
74.62
68.53
VPDS
86.21
90.53
92.98
90.86
93.13
90.14
80.90
83.44
67.50
89.50
76.78
87.97
85.83
indicates that the results for CPH are obtained by running its original code, while other results are obtained directly from their original papers.
SGD with momentum 0.9 and weight decay 1e-5. We set the initial learning rate 1e-3 and use batch size of 72. Training epoch is set to 20 for Digits and 100 for others. The hyperparameters are fixed as a = 0.9, 17 = 0.2, y = 0.2 across all datasets. All experiments are conducted on NVIDIA A30 GPU, We evaluate the retrieval performance using 4 standard metrics: mean average precision (MAP), precision-recall curve, Top-N precision curve and Top-N recall curve.
4.2 Comparison Results
Cross-Domain Retrieval. The MAP results of cross-domain retrieval tasks on three datasets are presented in Tables [2] As shown in Table [ the proposed VPDS method consistently achieves the best performance on most tasks, with a notable average improvement of 7. 7.25% over the second- best method CPH. Among the datasets, effice-Home presents a greater challenge due to its larger number of categories. The strong performance of VPDS on this dataset highlights its effectiveness in learning discriminative hash codes even in more complex category spaces. Additionally, tasks such as D-+A and W A from Office-31 require transferring knowledge from a source domain with significantly fewer samples to target domain with many more. On these tasks, VPDS yields

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Table MAP (%) of single-domain retrieval tasks with various bits on Office- Office-31 and Digits. The best and second-bex results are highlighted in bold and underlined.
Task
P-R
D
MNIST -USPS
Bit
32
64
128
32
64
128
32
64
128
ITQ
20.07
29.64
33.15
34.81
40.83
49.27
56.16
59.41
13.39
22.58
39.67
40.16
DSH
6.10
11.44
16.61
14.45
22.45
33.38
40.09
46.31
41.42
45.30
47.85
50.76
SGH
18.97
26.18
32.61
34.97
38.67
45.59
53.57
57.37
15.60
30.78
35.55
41.78
OCH
13.45
21.14
25.34
28.02
33.30
41.65
50.78
53.74
24.23
32,90
36.34
44.36
GraphBit
15,42
21.80
24.89
28.97
33.21
41.17
51.46
53,48
24.96
32.54
37.54
44.82
GTH-g
15.05
21.20
27.67
28.40
37.11
45.69
50.22
55.81
45.41
39.72
34.34
34.73
PWCF
24.80
34.03
37.98
39.14
49.94
53.05
59.08
62.35
50.21
49.41
60.06
64.00
DAPH
20.77
29.01
33.35
34.92
46.74
49.43
58.63
60.41
47.53
54.86
60.15
60.39
DHLing
27.81
36.05
40.91
44.07
52.08
56.43
60.17
63.44
51.25
50.48
63.13
67.02
PEACE
28.99
37.93
42.97
47.29
55.43
57.89
61.21
64.14
52.77
56.25
65.27
69.99
DANCE
31.37
37.64
44.13
48.93
54.42
58.02
63.09
67.91
52.65
55.98
66.81
70.47
IDEA
34.88
44.83
49.91
54.40
61.25
62.65
67.06
70.04
60.81
63.32
72.11
76.73
CPH
44.99
49.35
52.4S
51.40
60.60
62.
65.76
68.20
66.76
71.34
72.64
72.46
VPDS
47.20
54.86
59.12
59.45
71.22
77.66
85.90
85.52
80.12
88.34
90.35
89.30
substantial improvements of 9.84% and 4.74%, respectively, further demonstrating its robustness in handling severe domain shifts and data imbalance. Table 2 presents the retrieval performance on the Digits dataset across different hash code lengths. Our method outperforms all compared methods each code length, achieving an average improvement of 17.30% method. These results demonstrate the robustness and effectiveness of VPDS in learning compact and discriminative hash codes. Additional evaluations of cross-domain retrieval performance with varying code lengths on Office-Home and Office-31 are provided in Appendix A.1
the second-best
Single-Domain Retrieval. We evaluate single-domain retrieval performance three randomly selected tasks, each from a different dataset. As shown in Table VPDS outperforms all compared methods by a substantial margin, highlighting its effectiveness and strong generalization capability in single-domain retrieval scenarios.
4.3 Analyses.
Effect of VLM Assisted Pseudo-Labeling, To evaluate the effectiveness our proposed VLM assisted pseudo-labeling strategy, construct variants by replacing it with commonly used strategies: (1) VPDS w/ Pred: pseudo-labels are generated from the predictions of the adapted model, using only those with confidence scores above 0.9. (2) VPDS w/ Prot: pseudo-labels assigned based on the nearest class prototypes computed from the source domain. The experimental
results are shown in Table[4] we can see that VPDS outperforms the two variants it, demonstrating the effectiveness of our sseudo-labeling strategy.
Effect of Dual Space Adaptation. To assess the effectiveness of dual space adaptation approach, we design two variants: (1) VPDS w/ Hamming: utilizing only the Hamming space for learn- ing, discarding all information from the feature space. (2) VPDS w/ VLM Hamm: generating pseudo-labels based on predictions of the classifier in the Hamming space. As shown in Table [ both variants result in noticeable performance drop, highlighting the critical role of transferring knowledge from the semantically rich feature space to guide learning in the Hamming space.
Effect of Prototype- Prototype-Basee Based Global Alignment. The prototype-basee based global alignment strategy is evaluated by replacing with pratotype-based class-level alignment and MMD-based global align- ment, constructing VPDS w/ class_prot and VPDS w/ global MMDD As shown in Table VPDS outperforms the two variants, which demonstrates the superiority our alignment strategy over conventional class- and domain- alignment strategies.
Ablation Study. To assess the contribution of each component in VPDS, we conduct an ablation study by removing each component from VPDS. Details of variants are provided in Appendix A.2 The results, denoted as VPDS w/o, are shown Table We observe performance degradation in all ablation variants, demonstrating the effectiveness and necessity of each component.

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Table 4: MAP (%) of VPDS variants on
for cross-domain retrieval tasks.
Method
A-*R
R->A
C-+R
R->C
P->R
R R->P
Avg
VPDS w/ Pred
73.32
62.95
57.22
40.53
72,91
81.86
64.80
VPDS w/ Prot
68.68
58.73
43.54
35.64
61.56
74.32
57.08
VPDS w/ Hamming
56.90
51.97
13.54
21.33
45.77
50.18
39.95
VPDS w/ VLM Hamm
67.28
57.68
18.97
29.78
41.29
66.00
46.83
VPDS w/ class prot
71.57
62.05
61.01
40.19
75.59
82.74
65.53
VPDS w/ glohal_MMD
72.94
60.89
63.26
41.39
73.97
82.56
65.84
VPDS w/o CPp
70.23
60.25
60.91
40.15
73.49
80.02
64.18
VPDS w/o LPH
71.22
62.28
62.72
42.73
73.79
82.52
VPDS w/o Lp
70.16
63.16
63.16
40.99
75.30
82.12
65.82
VPDS w/o L; (feat)
69.28
62.07
55.32
37.58
71.89
76.86
62.17
VPDS w/o L. (Hamm)
70.68
61.05
58.79
38.73
73.60
80.92
63.97
VPDS w/o L;
63.83
56.60
46.95
32.95
67.60
75.24
57.20
VPDS w/o Le
71.92
60.67
58.79
40.76
74.60
80.23
64.50
VPDS
72.54
63.36
63.58
41.95
75.17
81,80
66.40
Table 5: Scalability (under the unsupervised hashing retrieval setting) and generalization (under the out-of-sample hashing retrieval setting) of VPDS.
Cross-Domaie Hashing
Tasks
A-R R-*A
C->R R R-C P R +P A-D A-
D-
D-
W A
W-
Unsupervised
54.18
50.48
32.31
33.14
65.50
68.22
67.63
70.43
67.38
88.81
62.65
88.50
Out-of Out-of-Sample
46.68
47.27
45.75
30.95
62.55
64.75 80.58
70.69
62.70
90.15
69.16
98.20
Single- Domain Hashing
Tasks
P-R
A-+
MNIST
Bit
16
32
64
128
16
32
64
128
16
32
64
128
Out-of Sample
42.22
47.70
52.24
51.68
69.46
78.09
83.75
88.81
87.27
76.22
74.83
88.33
Parameter Sensitivity. We evaluate the sensitivity of our method to all hyperparameters, including o, 1) and y. The results in Figure 2 show values, indicating that our method is robust to hyperparameter settings.
show that the performance remains stable across wide range of
Frecision-Recal Curve. We present the precision-recall curves on four cross-domain retrieval tasks randomly selected from Office-Homs, and Office-31 in Figure B The results show that our method works better than the compared methods. Additionally, the Top-N precision curves and Top-N recall curves are presented in Appendix
Scalability. Our framework can be readily extended to unsupervised hashing retrieval scenarios. Specifically, when labels for both source and target domain data are unavailable, our VLM assisted approach can generate pseudo-labels for samples in both domains. By replacing ground-truth labels of the source domain in VPDS with these generated pseudo-labels, our method can directly addresses the unsupervised hashing retrieval task. To validate this scalabil- ity, we evaluate VPDS under the unsupervised hashing retrieval setting on the Office-Home and Office-31 datasets. As shown in Table 5 our method outperforms all unsupervised basclines and most domain adaptive methods when compared to the results reported Table
Generalization. To assess the generalization (out-of-sample) ability of our method, we split the source domain data into a 70% training set and 30% retrieval set, and divide the target domain data into a 60% training set, a 30% retrieval set and 10% query set. We evaluate VPDS under this -fample setting, and the results are presented Tables 1 and 3 we observe that although the retrieval data is not involved in the training process, our method still outperforms most compared methods that leverage training data as the retrieval set. This validates the strong generalization capability of our approach.
Table [ Comparing to the results in

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100
100
Accurasy (h)
70
70-
60
60
60
P-48
P-R
50
P-R
KNST-USPS
MNIST-USPS
MNIST-USPS
0.9
0.95
1.0
(a)
(b)
(c)
Figure 2: Parameter sensitivity of VPDS (a) a, (b) and (c) 7. When 7 2 0.3, gradient vanishing issue emerges in task MNIST-+USPS, resulting in an incomplete curve in (c).
(a)
(b)
(c)
(d)
Figure 3: Precision-recall curves on four tasks randomly selected from Office-Home and Office-31.
(a) Feature of PEACE. (b) Hamming PEACE. (c) Feature of VPDS.
(d) Hamming of VPDS.
Figure 4: t-SNE visualization of A-D in feature and Hamming spaces.
Visualization. To illustrate the effect of our dual space adaptation approach, we employ t-SNE [47] to visualize the features in the feature space and hash codes in the Hamming space. We compare the visualization results with PEACE, which enforces strict relationship between the two spaces feature concatenation. As shown in Figure 4 our approach produces discriminative hash codes and yields better clustering the feature space compared to PEACE. These results validate our design of relaxing the constraints across spaces enable more flexible and effective cross-domain alignment.
Conclusion
This paper propose VPDS, a framework that performs independent domain alignment in both the feature and Hamming spaces exploit their complementary knowledge. To address potential bias from prototype representations, we introduce prototype-based global alignment strategy. Addition- ally, design VLM assisted pseudo-labeling strategy improve pseudo-label reliability, which can be seamlessly integrated with other unsupervised learning methods. Extensive experiments on multiple benchmarks validate the effectiveness and generalizability of our method.
Acknowledgments
This work was supported by the Natural Science Research Foundation of Jilin Province of China under Grant No. 20220101106JC, and the National Natural Science Foundation of China under Grant No. 62407010.

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References
[1] Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, and Amit K. mhyy-Chodhhury... 2021. Unsupervised Multi-source Domain Adaptation Without Access Source Data. In CVPR. 10102-10112.
[2] Shuanghao Bai, Min Zhang. Wanqi Zhou, Siteng Huang, Zhirong Luan, Donglin Wang. and Badong Chen. 2024. Prompt-Basee Based Distribution Alignment for Unsupervised Domain Adaptation. In AAAI, Vol. 38. 729-737.
[3] Hui Cui, Lihai Zhao, Fengling Li, Lei Zhu, Xiaohui Han, and Jingjing Li. 2024. Effective Comparative Prototype Hashing for Unsupervised Domain Adaptation. In AAAI, Vol. 38. 8329-8337.
[4] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenhorn, Xiaohua Zhai, Thomas Un- terthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR.
[5] Zhekai Du, Jingjing Li, Hongzu Su, Lei Zhu, and Ke Lu. 2021. Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation. In CVPR. 3937-3946.
[6] Zhckai Du, Xinyao Li, Fengling Li, Ke Lu, Loi Zhu, and Jingjing Li. 2024. Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation. In CVPR.
[7] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky. 2016. Nomain-Adversen Training of Neural Networks. JMLR 17, 59 (2016), 1-35.
[8] Chunjiang Gc, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, and Gao Huang. 2025. Domain Adaptation via Prompt Learning. IEEE TNNLS 36, (2025), 1160 1170.
[9] Yunchao Gong. Svetlana Lazebnik, Albert Gordo, and Florent Perronnin. 2013. Iterative Quantization: Procrustean Approach to Learning Binary Codes Large-Scale Image Retrieval. IEEE TPAMI 35, 12 (2013), 2916-2929.
[10] Qichen He, Siying Xiao, Mao Ye, Xiatian Zhu, Ferrante Neri, and Dongde Hou. 2023. Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation. In IJCAI. 819- 827.
[11] Tao He, Yuan-Fang Li, Lianli Gao, Dongxiang Zhang, and Jingkuan Song. 2019. One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Networks. In IJCAI. 2477-2483.
[12] Jiun Tian Hoe, Kam Woh Ng. Tianyu Zhang, Chee Seng Chan, Yi-Zhe Song, and Tao Xiang. 2021. One loss for all: Deep hashing with single cosine similarity based learning objective. In NeurIPS.
[13] Weihua Hu, Takeru Miyato, Seiya Tokui, Eicch Matsumoto, and Masashi Sugiyama. 2017. Learning discrete representations via information maximizing self-augmented training. In ICML. 1558-1567.
[14] Fuxiang Huang, Lei Zhang, and Xinbo Gao. 2022. Domain Adaptation Preconceived Hashing for Uncon- strained Visual Retrieval. JEEE TNNLS 33, 10 (2022), 5641-5655.
[15] Fuxiang Huang, Lei Zhang. Yang Yang. and Xichuan Zhou. 2020. Probability weighted compact feature for domain adaptive retrieval, In CVPR, 9582-9591.
[16] Jonathan J. Hull. 1994. A database for handwritten text recognition research. IEEE TPAMI 16, 5 (1994), 550-554.
[17] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Lee, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling Up Visual and Lasion-Language Representation Learning With Noisy Text Supervision. In JCML, Vol. 139, 4904-4916.
[18] Qing- Yuan Jiang and Wu-Jun Li. 2015. Scalable graph hashing with feature transformation. In I/CAL. 2248-2254.
[19] Zhongming Jin, Cheng Li, Yue Lin, and Deng Cai. 2014, Density Sensitive Hashing. IEEE T. Cybern 44, 8 (2014), 1362-1371.
[20] Zhengfeng Lai. Noranart Vesdapunt, Ning Zhou, Jun Wu, Cong Phuoc Huynh, Xuelu Li, Kah Kuen Fu, and Chen-Nee Chuah. 2023. PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsuper- vised Domain Adaptation. In /CCV. 16155-16165.

---

[21] Yann Lecun, Léon Botton, Yoshua Bengio, and Patrick Haffner: 1998. Gradient-basee based learning applied to document recognition. In Proc IEEE, Vol. 86. 2278-2324.
[22] Jingjing Li, Erpeng Chen, Zhengming Ding. Lei Zhu, Ke Lu, and Heng Tao Shen. 2021. Maximum Density Divergence for Domain Adaptation. IEEE TPAMI 43, 11 (2021), 3918-3930.
[23] Xinyao Li, Yuke Li, Zhekai Du, Fengling Li, Ke Lu, and Jingjing Li. 2024. Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation. CVPR. 33364-23374.
[24] Yungiang Li and Jan Gemert. 2021. Deep unsupervised image hashing by maximizing bit entropy. In AAAI Vol. 35. 2002-2010.
[25] Jian Liang, Dapeng Hu, and Jiashi Feng. 2020. Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In ICML. 6028-6039.
[26] Qinghong Lin, Xiaojun Chen, Qin Zhang, Shaotian Cai, Wenzhe Zhao, and Hongfa Wang. 2022, Deep unsupervised hashing with latent semantic components. In AAAI, Vol. 36. 7488-7496.
[27] Hong Liu, Rongrong Ji, Jingdong Wang, and Chunhua Shen. 2019. Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search. JEEE TPAMI 41, 4 (2019), 941-955.
[28] Hong Liu, Jianmin Wang. and Mingsheng Long. 2021. Cycle Self-Training for Domain Adaptation. In NewrIPS, Vol. 34. 22968-22981.
[29] Fuchen Long, Ting Yao, Qi Dai, Xinmei Tian, Jieho Luo, and Tao Mei. 2018, Deep Domain Adaptation Hashing with Adversarial Learning, In SIGIR. 725-734.
[30] Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan. 2015. Learning Transferable Features with Deep Adaptation Networks. In ICML, Vol. 37, 105.
[31] Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan. 2018. Conditional Adversarial Domain Adaptation. In NeurlPS.
[32] Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan. 2017. Deep Transfer Learning with Joint Adaptation Networks. In ICML, Vol. 70. 2208-2217
[33] Junyu Luo, Yusheng Zhao, Xiao Luo, Zhiping Xiao, Wei Ju. Li Shen, Dacheng Tao, and Ming Zhang. 2025. Cross-domain diffusion with progressive alignment for efficient adaptive retrieval. IEEE TIP 34 (2025), 1820-1834.
[34] Sinno Jialin Pan and Qiang Yang. 2010. Survey on Transfer Learning. IEEE TKDE 22, 10 (2010).
1345 1359.
[35] Hieu Pham, Zihang Dai, Qizhe Xie, and Quoe V. Le. 2021. Meta Pseudo Labels. In CVPR. 11557-11568.
[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwala, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Tlya Sutskever. 2021. Leam- ing Transferable Visual Models From Natural Language Supervision. In ICMI., Vol. 139, 8748-8763.
[37] Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell. 2010. Adapting Visual Category Models to New Domains. In ECCV, 213-226.
[38] Yang Shi, Xiushan Nie, Xingbo Liu, Li Zou, and Yilong Yin. 2022. Supervised adaptive similarity matrix hashing. IEEE TIP 31 (2022), 2755-2766.
[39] Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. In arXiv preprint 140i 1556.
[40] Mainak Singha, Harsh Pal, Ankit Jha, and Biplab Banerjee. 2023. AD-CLIP: Adapting Domains in Prompt Space Using CLIP. In /CCV Workshops. 4355-4364.
[41] Baochen Sun, Jiashi Feng, and Kate Saenko. 2016. Return of Frustratingly Easy Domain Adaptation. In AAAI, Vol. 30. 2065.
[42] Baochen Sun and Kate Sacnko. 2016. Deep CORAL: Corrolation Alignment for Deep Domain Adapta- tion. In ECCV Workshops. 443-450.
[43] Tao Sun, Cheng Lu, and Haibin Ling. 2023. Domain Adaptation with Adversarial Training on Penultimate Activations. In AAAI, Vol. 37. 9935-9943.
12

---

[44] Tao Sun, Cheng Lu, Tianshuo Zhang, and Haibin Ling, 2022. Safe Self-Refinement for Transformer- Based Domain Adaptation. In CVPR. 7191-7200.
[45] Song Tang, Wenxin Su, Yan Gan, Mao Ye, Jianwei Zhang, and Xiatian Zhu. 2025. Proxy Denoising for Source-Free Domain Adaptation. In ICI.R.
[46] Rong-Cheng Tu, Xian-Ling Mao, Kevin Qinghong Lin, Chengfei Cai, Weize Qin. Wei Wei, Hongfa Wang. and Heyan Huang. 2023. Unsupervised hashing with semantic concept mining, In SIGMOD.
[47] Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using I-SNE, JMI.R 9, 86 (2008), 2579-2605.
[48] Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. 2017. Deep Hashing Network for Unsupervised Domain Adaptation. In CVPR. 5018-5027.
[49] Haixin Wang, Jinan Sun, Xiao Luo, Wei Xiang, Shikun Zhang, Chong Chen, and Xian-Sheng Hua. 2023.
Toward Effective Domain Adaptive Retrioval. IEEE TIP 32 (2023), 1285-1299.
[50] Haixin Wang, Jinan Sun, Shikun Zhang, Wei Xiang, Chong Chen, Xian-Sheng Hua, and Xiao Luo. 2023.
DANCE: Learning A Domain Adaptive Framework for Deep Hashing. In ACM MM. 3319-3330.
[51] Haixin Wang, Hao Wu, Jinan Sun, Shikun Zhang, Chong Chen, Xian-Sheng Hua, and Xiao Luo. 2023. IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval. In NewrIPS, Vol. 36.
57256-57275.
[52] Jingdong Wang, Ting Zhang. Jingkuan Song, Nicu Sebe, and Heng Tao Shen. 2018 A Survey on Learning to Hash. IEEE TPAMI 40, 4 (2018), -90.
[53] Ziwei Wang, Han Xiao, Yueqi Duan, Jie Zhou, and Jiwen Lu. 2023. Learning Deep Binary Descriptors via Bitwise Interaction Mining. IEEE TPAMI 45, (2023), 1919-1933.
[54] Haifeng Xia, Taotao Jing, Chen Chen, and Zhengming Ding. 2021. Semi- supervipe Domain Adaptive Retrieval via Discriminative Hashing Learning. In ACM MM. 3853-3861.
[55] Zhiqing Xiao, Haobo Wang, Ying Jin, Lei Fong, Gang Chen, Fei Huang, and Junbo Zhao. 2023. SPA: Graph Spectral Alignment Perspective for Domain Adaptation. In NewrIPS, Vol. 36. 37252-37272.
[56] Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang. Qi Tian, and Wenjun Zhang. 2020. Adversarial Domain Adaptation with Domain Mixup. In AAAI, Vol. 34. 4502-6509.
[57] Chenggang Yan, Biao Gong, Yuxuan Wei, and Yue Gao. 2020. Deep multi-view enhancement hashing for image retrieval. IEEE TPAMI 43, 4 (2020), 1445-1451.
[58] Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, and Yinghuan Shi. 2023. Revisiting Weak-to- to-Sonng Consistency in Supervu Semantic Segmentation. In CVPR. 7236-7246.
[59] Li Yuan, Tao Wang. Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, and Jiashi Feng. 2020. Central Similarity Quantization for Efficient Image and Video Retrieval. In CVPR. 3083-3092.
[60] Zhongqi Yue, Hanwang Zhang, and Qianru Sun. 2023. Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation. NeurlPS, Vol. 36. 26991-27004.
[61] Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wa, Jindong Wang. Manabu Okumura, and Takahiro Shinozaki. 2021. FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling. In NewrlPS, Vol. 34. 18408-18419.
[62] Lei Zhang, Ji Liu, Yang Yang, Fuxiang Huang Feiping Nic, and David Zhang. 2020. Optimal Projection Guided Transfer Hashing for Image Retrieval. JEEE TCSVT 30, 10 (2020), 3788-3802.
[63] Qi Zhang, Liang Hu, Longbing Cao, Chongyang Shi, Shoujin Wang, and Dora D Liu. 2022. A proba- bilistic code balance constraint with compactness and informativeness enhancement for deep supervised hashing, In IJCAI. 1651-1657.
[64] Chaoyang Zhou, Zengmao Wang, Bo Du, and Yong Luo. 2024. Cycle Self-Refinement for Multi-Soure Domain Adaptation. AAAI, Vol. 38. 17096-17104.
[65] Lei Zhu, Chaoqun Zheng, Weili Guan, Jingjing Li, Yang Yang. and Heng Tao Shen. 2024. Multi-Modal Hashing for Efficient Multimedia Retrieval: Survey, IEEE TKDE 36, (2024). 260.
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Appendix
A
Additional Experimental Results
A.1
Performance of Cross-Domain Retrieval
Table 6 presents the MAP results for cross-domain retrieval tasks under varying hash code lengths. Across all code lengths, VPDS consistently outperforms the compared methods on most tasks and achieves notable improvement average performance, demonstrating its robustness and effective- ness in different hash settings.
Table 6: MAP (%) of cross-domain retrieval tasks with various bits Office-Hone and Office-31.
Office-Home
Office-31
Method
Bit
A
RR
R
>
R
R-C
P
R
R
+P
A
>
A
-W
D D-
D D
W->A
W->D|
Avg
10.20
9.51
6.04
5.90
10.84
11.08
26.25
11.85
15.76
34.40
16.14
31.79
15.81
GTH-g
32
13.08
13,93
7,86
9,52
15.28
16.17
28.35
15.76
21.15
41.36
19.23
42.86
20.38
128
16.51
19.52
8.53
13.92
20.81
21.24
31.68
20.55
21.93
50.09
20.17
53.54
24.87
16
9.54
8.18
6.17
6.30
11.32
10.81
24.86
11.94
19.02
34.15
14.66
40.58
16.46
GTH-h
32
13.43
12.67
7.77
8.97
15.71
15.36
24.65
15.56
20.98
41.67
17.97
42.33
19.76
13.78
19.73
8.57
14.54
21.16
20.16
32.01
22.16
22.56
51.62
21.55
51.57
24.20
11.92
14.46
8.16
8.12
17.11
14.37
22.46
15.94
19.69
52.39
19.44
34.01
19.84
DAPH
17.72
19.63
10.48
10.64
22.47
20.25
25.15
19.09
21.99
54.28
22.00
36.58
23.36
128
22.27
23.78
14.32
13.39
28.25
25.34
32.90
27.49
29.11
64.25
26.58
47.59
29.61
62.19
53.41
53.47
35.64
64.18
66.65
59.54
52.54
43.47
90.86
55.53
98.33
61.32
CPH
32
67.87
60.16
56.71
39.71
68.31
71.72
64.00
56.33
49.34
95.04
59,18
99.49
65.66
128
72.17
64.62
53.25
42.71
72.89
75.50
69.20
61.18
54.89
97.54
61.49
99.99
68.79
16
67.05
54.63
57.28
37.66
68.67
78.25
77.36
84.11
63.67
98.78
68.39
100
71.32
VPDS
32
70.67
56.65
62.07
41.89
73.42
82.18
83.15
88.85
68.7
98.88
67.96
98.43
74.40
70.74
61.86
61.86
40.84
73.2
82.42
85.67
88.76
72.84 84
98.80
64.46
99.86
75.11
A.2
Details of Ablation Variants
In Section we perform an ablation study to evaluate the contribution of each component in VPDS. Specifically, we construct seven variants by removing individual components from the full model, including:
(1) VPDS w/o Lpp: removing prototype-base based alignment in the feature space.
(2) VPDS w/o Lpy removing prototype-based alignment in the Hamming space.
(3) VPDS w/o Lp: removing prototype- prasetype-based based alignments both the feature and Hamming spaces.
(4) VPDS w/o L: (feat): excluding the information maximization technique in the feature space.
(5) VPDS w/o L: (Hamm): excluding the information maximization technique in the Hamming space.
(6) VPDS w/o L:: excluding the information maximization technique in both the feature and Ham- ming spaces.
(7) VPDS w/o Le: removing the pseudo-label-basse self-supervised learning objective.
A.3 Top-N Precision Curve and Top-N Recall Curve
The Top-N precision curves and Top-N recall curves for four randomly selected tasks are shown in Figures and G Our method consistently achieves higher precision and recall across different values of N, highlighting its effectiveness in cross- retrieval tasks.
21

---

(a)
(h)
(c)
(d)
Figure 5: Top-N precision curves four tasks randomly selected from Office-Home and Office-31.
1.
(a)
(b)
(c)
(d)
Figure 6: Top-N recall curves on four tasks randomly selected from Office-Home and Office-31.
A.4 Time and Memory Consumption
To evaluate the computational efficiency of our method, compare its time and memory consump- tion against PEACE, which also exploits both feature and Hamming spaces. While CPH similarly leverages dual-space information, it uses pre-encoded features input, thereby bypassing the fea- ture extraction stage. As shown in Table we report the training time over 10 epochs using fixed batch size. Although our method incurs higher training time compared to PEACE, it exhibits ob- viously lower memory consumption. Considering the obvious performance improvements observed in prior metrics such MAP, the increased computational cost is reasonable trade-off and can be acceptable.
Table 7: Time and memory consumption.
Office-Home
Method
A-R
R-+A
C-R
R-C
P-+R
R-PP
Avg
PEACE
175.93
160.62
277.41
280.05
278.08
288.44
243.42
Time (s)
VPDS
193.26
174.29
308.36
299.77
303.58
302.57
263.64
Memory (MiB)
PEACE
17,688
VPDS
9,840
Office-31 31
Method
A-+D
A-W
D-
D-W
W-+A
W-+
Avg
PEACE VPDS
37.82 37.94
55.08
39.08
39.97
59.09
37.22
44.71
Time (s)
53.76
39.80
39,53
60.84
37,92
44.97
Memory (MiB)
PEACE VPDS
17,686
9,864
B Broader Impacts and Limitations
This paper proposes novel domain adaptive retrieval method, which achieves substantial perfor- mance gains over existing methods. We propose VLM assisted pseudo-labeling strategy that enhances the reliability of pseudo-labels. This strategy can be seamlessly integrated with other unsupervised and cross-domain learning approaches, thereby improving performance in scenarios with limited labeled data. Additionally, we present dual space adaptation approach that leverages
22

---

the semantically rich feature space to guide learning of discriminative hash codes, while eliminating the explicit constraints from Hamming space to feature space. providing new direction for advanc- ing cross-domain retrieval. In this paper, we only focus on adaptive retrieval across two domains, while adaptive retrieval across multiple domains is more practical in real-world, thus we will explore such scenarios in our future work.
23


Reviews:
[{"review_id": "i3GG9bKqAA", "reviewer": "Reviewer_WN9s", "summary": "The paper proposes a novel method called Vision-language model assisted Pseudo-labeling and Dual Space adaptation (VPDS) for unsupervised domain adaptive hashing retrieval. The goal is to efficiently map samples from a labeled source domain and an unlabeled target domain into compact binary codes in the Hamming space, ensuring semantically similar samples are mapped to nearby points. VPDS leverages a pre-trained vision-language model (VLM) to calibrate pseudo-labels, reducing bias and enhancing reliability. It also introduces a dual space adaptation approach, performing independent alignment in both the feature space and the Hamming space to exploit their complementary strengths. The method is evaluated on three benchmark datasets, demonstrating superior performance in both cross-domain and single-domain retrieval tasks.", "questions": "1. How does the method perform on larger datasets with more classes and samples? Can the authors provide insights into the scalability of VPDS and any potential challenges?", "limitations": "None", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is technically sound, with a well-structured approach to addressing the challenges of domain adaptation in hashing retrieval.\n\n2. The paper is well-written and organized. The authors provide a clear description of the problem, the proposed method, and the experimental setup. \n\nWeakness:\n\n1. The paper acknowledges that VPDS incurs higher training time compared to some existing methods like PEACE. While the performance gains justify the increased computational cost, this may limit its applicability in real-time or resource-constrained scenarios.\n\n2. The experiments are conducted on relatively small datasets. It would be beneficial to evaluate the method on larger-scale datasets to assess its scalability and robustness in more complex scenarios.", "was_revised": true, "final_justification": "The response has addressed most of my concerns, I'd like to raise my score.", "rebuttal": "**To Reviewer WN9s**:\n\n**Q1**: How does the method perform on larger datasets with more classes and samples? \n\n**A1**: We evaluate our method on the large-scale DomainNet dataset, which comprises 0.6 million images across 345 classes, distributed in 6 domains. Due to time constraints, we select 4 domains and construct 6 tasks for evaluation. The MAP results are presented in Table 7. \n\nDomainNet is widely recognized as one of the most challenging unsupervised domain adaptation datasets, its obvious domain shifts and large number of classes lead to substantially lower MAP results compared to the other datasets in the main text. Notably, our method outperforms the recently proposed PEACE, highlighting its robustness and effectiveness in handling complex large-scale scenarios.\n\n**Table 7**: MAP (%) of cross-domain retrieval tasks on DomainNet.\n\n| Method | rel$\\rightarrow$skt | rel$\\rightarrow$clp | rel$\\rightarrow$pnt | clp$\\rightarrow$rel | pnt$\\rightarrow$skt | skt$\\rightarrow$pnt | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | \n| PEACE | 11.08 | 28.86 | 29.32 | 34.02 | 10.48 | 14.01 | 21.30 |\n| VPDS | 29.55 | 38.37 | 36.82 | 47.34 | 30.40 | 33.63 | 36.02 |\n\n\n**Q2**: Can the authors provide insights into the scalability of VPDS and any potential challenges?\n\n**A2**: We demonstrate the scalability of VPDS from two key perspectives:\n\n1. The VLM assisted pseudo-labeling module in VPDS can be seamlessly integrated with other unsupervised methods, providing more reliable pseudo-labels to guide model training.\n\n2. Our framework can be readily extended to unsupervised hashing retrieval scenarios. Specifically, when labels for both source and target domain data are unavailable, our VLM assisted pseudo-labeling approach can generate pseudo-labels for samples in both domains. By replacing the ground-truth labels of the source domain in VPDS with these generated pseudo-labels, our method can directly addresses the unsupervised hashing retrieval task.\n\nTo validate this scalability, we evaluate VPDS under the unsupervised hashing retrieval setting on the Office-Home and Office-31 datasets. As shown in Table 8, our method outperforms all unsupervised baselines and most domain adaptive methods when compared to the results reported in Table 1 of the main text.\n\n**Table 8**: Performance of VPDS in addressing unsupervised cross-domain hashing retrieval.\n\n| Method | A$\\rightarrow$R | R$\\rightarrow$A | C$\\rightarrow$R | R$\\rightarrow$C | P$\\rightarrow$R | R$\\rightarrow$P | A$\\rightarrow$D | A$\\rightarrow$W | D$\\rightarrow$A | D$\\rightarrow$W | W$\\rightarrow$A | W$\\rightarrow$D | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| VPDS | 54.18 | 50.48 | 32.31 | 33.14 | 65.50 | 68.22 | 67.63 | 70.43 | 67.38 | 88.81| 62.65 | 88.5 | 62.44 |"}, {"review_id": "b5vyNbKwD2", "reviewer": "Reviewer_Efug", "summary": "This paper proposes a novel domain adaptive hashing method called VPDS, by adopting vision-language model (VLM) assisted pseudo-labeling and dual space adaptation. Motivated by the strong zero-shot generalization capabilities of pre-trained VLMs, VPDS leverages VLMs to calibrate pseudo-labels, thereby mitigating pseudo-label bias. Furthermore, to simultaneously utilize the semantic richness of high-dimensional feature space and preserve discriminative efficiency of low-dimensional Hamming space, VPDS adopts a dual space adaptation approach that performs independent alignment within each space. Experiments on three benchmark datasets are used to demonstrate the effectiveness of VPDS.", "questions": "Please refer to the weaknesses, especially about the out-of-sample ability evaluation.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\nThe studied problem about domain adaptive hashing is interesting.\n\nThe paper is well written with good organization. \n\nThe proposed method is novel. \n\nExperiments on real datasets show that the proposed method can achieve far better performance than existing methods. \n\n\n\nWeaknesses\n\nIn the objective function (Line 223), there are no coefficients for L_s and L_P, which means that L_s and L_P have equal weight. This is typically not reasonable. The authors should give detailed explanation about the reasonability of this choice. \n\nIn Line 217, \\beta=e/E, where E is the total number of training iterations. In real applications, the total number of training iterations cannot be known in advance during the training process. It is actually a hyper-parameter which is typically determined by the results on evaluation set. \n\nFrom the first paragraph of Section 3 and the experimental setup, it seems that the authors only focus on the setting that the retrieval set is constructed from the training set. The setting that the retrieval set is not necessarily from the training set is also a typical setting in hashing based retrieval, which can better verify the generalization (out-of-sample) ability of the hashing methods. \n\nFor parameter sensitivity in Figure 2, the results when the hyper-parameter is zero are very important and informative, which are omitted in Figure 2.", "was_revised": true, "final_justification": "The authors have addressed my concerns in the rebuttal. Hence, I raise my score.", "rebuttal": "**To Reviewer Efug**:\n\n**Q1**: In the objective function (Line 223), there are no coefficients for L_s and L_P, which means that L_s and L_P have equal weight. The authors should give detailed explanation about the reasonability of this choice.\n\n**A1**: We consider that $L_s$ is critical for capturing knowledge from the labeled source domain, while $L_P$ plays a key role in reducing the domain gap for effective knowledge transfer, which are the two key components of domain adaptive retrieval. Therefore, we initially assign equal weights to these two components, assuming they hold comparable importance.\n\nHowever, this insightful comment inspires us to explore the trade-off between $L_s$ and $L_P$ more thoroughly. To this end, we introduce a hyperparameter $\\lambda$ to scale $L_P$, and conduct an experiment to evaluate VPDS across varying $\\lambda$ values. As shown in Table 9, the overall performance of VPDS exhibits low sensitivity to variations in $\\lambda$.\n\n**Table 9**: VPDS with various $\\lambda$ values.\n\n| Method | 0.2 | 0.4 | 0.6 | 0.8 | 1.0 | 1.2 | 2.0 | \n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | \n| A$\\rightarrow$D | 88.02 | 88.06 | 87.79 | 85.23 | 87.22 | 85.95 | 89.37 |\n| P$\\rightarrow$R | 72.79 | 72.80 | 71.91 | 74.17 | 75.17 | 74.53 | 72.42 |\n\n\n**Q2**: In Line 217, $\\beta=e/E$, where E is the total number of training iterations. In real applications, the total number of training iterations cannot be known in advance during the training process.\n\n**A2**: To address this issue, we propose an alternative solution: $\\beta=1-exp(-e)$, where $e$ denotes the current iteration number. This formulation eliminates the need for the hyperparameter $E$. To evaluate this approach, we conduct experiments on the Office-31 dataset. The results in Table 10 show that this alternative outperforms all compared methods in Table 1 of the main text, demonstrating the effectiveness of this approach for calculating $\\beta$.\n\n**Table 10**: Cross-domain retrieval performance of VPDS with $\\beta=1-exp(-e) $.\n| Method | A$\\rightarrow$D | A$\\rightarrow$W | D$\\rightarrow$A | D$\\rightarrow$W | W$\\rightarrow$A | W$\\rightarrow$D | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | \n| VPDS ($\\beta=1-exp(-e)$) | 84.27 | 84.45 | 67.95 | 98.87 | 63.47 | 99.93 | 83.16 |\n\n\n**Q3**: From the first paragraph of Section 3 and the experimental setup, it seems that the authors only focus on the setting that the retrieval set is constructed from the training set. The setting that the retrieval set is not necessarily from the training set is also a typical setting in hashing based retrieval, which can better verify the generalization (out-of-sample) ability of the hashing methods.\n\n**A3**: To assess the generalization (out-of-sample) ability of our method, we split the source domain data into a 70% training set and a 30% retrieval set, and we divide the target domain data into a 60% training set, a 30% retrieval set and a 10% query set. We evaluate VPDS under this out-of-sample setting, and the results are presented in Tables 11 and 12. Comparing to the results in Tables 1 and 4 of the main text, we observe that although the retrieval data is not involved in the training process, our method still outperforms most compared methods that leverage training data as the retrieval set. This validates the strong generalization capability of our approach.\n\n**Table 11**: Cross-domain retrieval performance of VPDS under out-of-sample setting.\n\n| Method | A$\\rightarrow$R | R$\\rightarrow$A | C$\\rightarrow$R | R$\\rightarrow$C | P$\\rightarrow$R | R$\\rightarrow$P | A$\\rightarrow$D | A$\\rightarrow$W | D$\\rightarrow$A | D$\\rightarrow$W | W$\\rightarrow$A | W$\\rightarrow$D | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| VPDS | 46.68 | 47.27 | 45.75 | 30.95 | 62.55 | 64.75 | 80.58 | 70.69 | 62.70 | 90.15 | 69.16 | 98.20 | 64.12 |\n\n**Table 12**: Single-domain retrieval performance of VPDS under out-of-sample setting.\n\n| Bit | P$\\rightarrow$R  | A$\\rightarrow$D | MNIST$\\rightarrow$USPS | \n| ---- | ---- | ---- | ---- | \n| 16 | 42.22 | 69.46 | 87.27 |\n| 32 | 47.70 | 78.09 | 76.22 |\n| 64 | 52.24 | 83.75 | 74.83 |\n| 128 | 51.68 | 88.81 | 88.33 |\n\n\n**Q4**: For parameter sensitivity in Figure 2, the results when the hyperparameter is zero are very important and informative, which are omitted in Figure 2.\n\n**A4**: Our framework involves three hyperparameters ($\\eta$, $\\gamma$, $\\alpha$) as introduced in the main text, and a hyperparameter $\\lambda$ for $L_P$ as suggested in Q2. Setting $\\eta=0$, $\\gamma=0$ or $\\lambda=0$ corresponds to removing the loss functions $L_t$, $L_i$ or $L_P$ from the overall training objective, which aligns with the variants VPDS w/o $L_t$，VPDS w/o $L_i$ and  VPDS w/o $L_P$ in Table 3 of the main text, respectively. The results in Table 3 indicate that $\\eta=0$, $\\gamma=0$ or $\\lambda=0$ leads to performance drop, demonstrating the importance of these losses.\n\nWe supplement experiments on the sensitivity of VPDS to $\\alpha$, including $\\alpha=0$. As shown in Table 13, $\\alpha=0$ leads to a decrease in performance compared to our setting of $\\alpha=0.9$, demonstrating that our hyperparameter setting is appropriate.\n\n**Table 13**: Performance of VPDS with varying $\\alpha$ values.\n\n| $\\alpha$ | 0  | 0.1 | 0.5 | 0.8 | 0.9 | 0.95 |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | \n| A$\\rightarrow$D | 86.28 | 83.44 | 81.59 | 81.46 | 87.22 | 82.77 |\n| P$\\rightarrow$R | 74.19 | 73.57 | 75.58 | 76.00 | 75.17 | 74.14 |"}, {"review_id": "QOkC85EMJj", "reviewer": "Reviewer_LZaa", "summary": "This paper studies an important question of Domain Adaptive Hashing Retrieval and proposes a new method named Vision-language model assisted Pseudo-labeling and Dual Space adaptation (VPDS). In particular, this method leverages VLMs to calibrate pseudo-labels, thereby mitigating pseudo-label bias. Moreover, it introduces a dual space adaptation approach that performs independent alignment within each space. Experiments validate the superiority of the proposed method.", "questions": "See above.", "limitations": "NA", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Pros.\n\n1. This paper is well organized and clearly written. \n\n2. The studied problem is quite important in this era. \n\n3. The experiments are extensive and helpful to validate the effectiveness of the model. \n\nCons. \n\n1. The ablation paper should be moved to the main part since it is quite important.\n\n2. The most recent baseline comes from 2024. I suggest authors include more SOTAs in the recent years for comparison, such as [1,2,3].\n\n3. I suggest authors include a clear algorithm in the main body to make the paper more clear.\n\n4. The difference between domain adaptive hashing retrieval and other settings should be discussed. Can this method be extended to more complicated scenarios, such as test-time retrieval and unsupervised hashing retrieval? \n\n\n[1] Zou, Qiang, Shuli Cheng, and Jiayi Chen. \"PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[2] Luo J, Zhao Y, Luo X, et al. Cross-domain diffusion with progressive alignment for efficient adaptive retrieval[J]. IEEE Transactions on Image Processing, 2025.\n\n[3] Ni J, Cui H, Zhao L, et al. Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval[C]//ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025: 1-5.", "was_revised": false, "final_justification": "", "rebuttal": "**To Reviewer LZaa**:\n\n**Q1**: The ablation paper should be moved to the main part since it is quite important.\n\n**A1**: The results of our ablation study are presented in Table 3 of the main text, where we evaluate the following variants: VPDS w/o $L_{P_F}$, VPDS w/o $L_{P_H}$, VPDS w/o $L_{P}$, VPDS w/o $L_{i}$ (feat), VPDS w/o $L_{i}$ (Hamm), VPDS w/o $L_{i}$ and VPDS w/o $L_{t}$. Detailed descriptions of these variants can be found in the Appendix.\n\n\n**Q2**: The most recent baseline comes from 2024. I suggest authors include more SOTAs in the recent years for comparison.\n\n**A2**: We have included a comparison with COUPLE, which is proposed in:\nJ. Luo, Y. Zhao, X. Luo, et al. Cross-domain diffusion with progressive alignment for efficient adaptive retrieval. IEEE Transactions on Image Processing, 2025. \n\nAs shown in Tables 14 and 15, our method VPDS outperforms COUPLE, a state-of-the-art approach published in 2025.\n\n**Table 14**: Comparison with COUPLE on Office-Home and Office-31.\n\n| Method | A$\\rightarrow$R | R$\\rightarrow$A | C$\\rightarrow$R | R$\\rightarrow$C | P$\\rightarrow$R | R$\\rightarrow$P | A$\\rightarrow$D | A$\\rightarrow$W | D$\\rightarrow$A | D$\\rightarrow$W | W$\\rightarrow$A | W$\\rightarrow$D | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| COUPLE | 54.14 | 54.35 | 49.24 | 41.39 | 63.94 | 64.29 | 50.27 | 59.32 | 56.04 | 88.90 | 56.35 | 85.26 | 60.29 |\n| VPDS | 72.54 | 63.36 | 63.58 | 41.95 | 75.17 | 81.80 | 87.22 | 91.40 | 65.88 | 98.91 | 64.88 | 100 | 75.56 |\n\n**Table 15**: Comparison with COUPLE on MNIST$\\rightarrow$USPS (columns 2-7) and USPS $\\rightarrow$MNIST (columns 8-13) .\n\n| Bit | 16 | 32 | 48 | 64 | 96 | 128 | 16 | 32 | 48 | 64 | 96 | 128 | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| COUPLE | 60.56 | 66.05 | 66.23 | 67.98 | 73.02 | 75.12 | 63.28 | 64.94 | 67.44 | 70.19 | 72.87 | 74.62 | 68.53 |\n| VPDS | 86.21 | 90.53 | 92.98 | 90.86 | 93.13 | 90.14 | 80.90 | 83.44 | 67.50 | 89.50 | 76.78 | 87.97 | 85.83 |\n\n\n**Q3**: I suggest authors include a clear algorithm in the main body to make the paper more clear.\n\n**A3**: The algorithm of our method is formalized as follows:\n\n**Algorithm 1. VPDS**  \n\n**Input**：Labeled source domain $D^s$, unlabeled target domain $D^t$, training iterations T, hyperparameters $\\alpha$, $\\eta$ and $\\gamma$. \n\n**Output**：A domain adaptive hashing retrieval model, consisting of a feature encoder $F$ and a hash encoder $H$.\n\n1. Randomly initialize memory banks $M_F^s$, $M_F^t$, $M_H^s$ and $M_H^t$.\n\n2. **for** $t = 1, 2, ..., T$ **do**\n\n    a. Sample a batch  of $B$ samples from $D^s$ and $D^t$.\n\n    b. Generate prediction results using the current model.\n   \n    c. Obtain pseudo-labels for target domain samples via Eq. (10).\n\n    d. Calculate losses $L_s$, $L_t$ and $L_i$ according to Eqs. (1), (2) and (3), respectively.\n\n    e. Compute class prototypes $P_F^s$ and $P_F^t$ in the feature space following Eq. (5).\n\n    f. Compute class prototypes $P_H^s$ and $P_F^t$ in the Hamming space using the same approach as for $P_F^s$ and $P_F^t$.\n\n    g. Update $M_F^s$ and $M_F^t$ according to Eq. (4).\n\n    h. Update $M_H^s$ and $M_H^t$ using the same update rule as for $M_F^s$ and $M_F^t$.\n\n    i. Calculate loss $L_P$ using Eq. (8).\n\n    j. Compute the total loss $L$ via Eq. (11).\n\n    k. Update model parameters by minimizing $L$.\n\n14. **return** Optimized $F$ and $H$.\n\n\n**Q4**: The difference between domain adaptive hashing retrieval and other settings. \n\n**A4**: We clarify the key distinctions between domain adaptive hashing retrieval and other settings (supervised hashing retrieval and unsupervised hashing retrieval) as follows:\n\n(1) Supervised hashing retrieval relies on labeled training data from the target domain. Unsupervised hashing retrieval utilizes only unlabeled data with no reliance on semantic annotations. Domain adaptive hashing typically leverages labeled data from the source domain and unlabeled data from the target domain.\n\n(2) Both supervised and unsupervised hashing retrieval operate under a single-domain setting, namely, assuming training and testing data are drawn from the same distribution. Domain adaptive hashing retrieval is explicitly designed for cross-domain scenarios, where training and testing data can follow different distributions.\n\n\n**Q5**: Can this method be extended to more complicated scenarios, such as test-time retrieval and unsupervised hashing retrieval?\n\n**A5**: Our method can be readily extended to unsupervised hashing retrieval scenarios. Specifically, when labels for both source and target domain data are unavailable, our VLM assisted pseudo-labeling approach can be applied to generate pseudo-labels for samples in both domains. By replacing the ground-truth labels of the source domain in VPDS with these generated pseudo-labels, our framework can directly address the unsupervised hashing retrieval task. \n\nWe evaluate our method under the unsupervised hashing retrieval setting, with results presented in Table 16.  Compared to the results reported in Table 1 of the main text, our method outperforms all unsupervised baselines and most domain adaptive methods.\n\n**Table 16**: Performance of VPDS in addressing unsupervised cross-domain hashing retrieval.\n\n| Method | A$\\rightarrow$R | R$\\rightarrow$A | C$\\rightarrow$R | R$\\rightarrow$C | P$\\rightarrow$R | R$\\rightarrow$P | A$\\rightarrow$D | A$\\rightarrow$W | D$\\rightarrow$A | D$\\rightarrow$W | W$\\rightarrow$A | W$\\rightarrow$D | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| VPDS | 54.18 | 50.48 | 32.31 | 33.14 | 65.50 | 68.22 | 67.63 | 70.43 | 67.38 | 88.81| 62.65 | 88.50 | 62.44 |"}, {"review_id": "a95DsQ7NJG", "reviewer": "Reviewer_3ikP", "summary": "This paper proposes the VPDS framework, which utilizes the strong generalization ability of VLM (CLIP) to enhance the reliability of pseudo labels in the target domain through progressive weighting. It independently aligns the feature space (high-dimensional semantics) and Hamming space (low dimensional binary code), avoiding the spatial coupling problem of traditional methods. The best accuracy has been achieved on cross domain datasets such as Office-Home.", "questions": "1. Why did the ablation experiment not compare the results of removing CLIP generated pseudo labels? Have you considered the issue of using CLIP to add additional information leading to improved accuracy? Have you compared the related increased time consumption?\n2. The article emphasizes dual space decoupling, but Hamming space training relies on pseudo labels in the feature space. Is there any information leakage? What’s the essential difference between the coupled alignment (feature+hash joint optimization) of CPH and the decoupled alignment in this paper \n3. The architecture and optimization objectives proposed in the article are quite similar to CLIP the Divergence: Language-guided Unsupervised Domain Adaptation , except for the addition of a hash encoder and classifier for the hash task. The related work is also not mentioned. Is there any reference?", "limitations": "The article did not conduct ablation experiments on removing CLIP to generate pseudo labels. Considering the powerful feature extraction ability of CLIP pre-trained models, a unified feature space was introduced in cross domain problems, which may have a significant impact on the results. At the same time, new computational complexity was introduced. Although each image is only inferred once, CLIP itself has a large number of visual and text encoding parameters, so the increase in computational complexity cannot be ignored. Suggest adding ablation experiments that remove CLIP generated pseudo labels and adding a comparison of CLIP's time consumption(especially compared to CPH, not only PEACE at the additional part).", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Quality: The technology submitted in the article is quite reasonable, and the theoretical analysis and experimental results look good, but the comparison of related works is not sufficient (such as using CLIP to generate pseudo labels, only comparing the two variants by replacing it with commonly used strategies, without comparing other works, such as PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain  Adaptation, CLIP the Divergence: Language-guided, etc. At the same time, compared with other methods such as CPH, additional CLIP pre-trained weights were introduced to generate pseudo labels, which increased the information and computational complexity. The article did not discuss this impact, and the ablation experiment did not compare the results of removing clip generated pseudo labels.\n\nClarity: The overall plan and experimental results of the article are written clearly and organized reasonably. There is a contradiction in the article's emphasis on dual space decoupling, while Hamming space training relies on pseudo labels in the feature space. Is there information leakage without achieving true decoupling?\n\nSignificance: Cross-domain is quite common in hash tasks and has certain application value. There are many related works on using CLIP to generate pseudo labels (such as PADCLIP, CLIP the Divergence), and there are also many works on aligning in Hamming space (such as CPH). I feel that the innovative points proposed in this article are not particularly original, only simple combinations.\n\nOriginality:  The architecture and optimization objectives proposed in the article are similar to CLIP the Divergence: Language-guided Unsupervised Domain Adaptation, only adding a hash encoder and classifier for the hash task, and there is no mention of that paper in related work. At the same time, the essential difference between the coupled alignment (feature+hash joint optimization) of CPH and the decoupled alignment in this paper has not been clearly compared (Section 3.1 only describes its own method).", "was_revised": true, "final_justification": "My questions were resolved.", "rebuttal": "**To Reviewer 3ikP**:\n\n**Q1**: Why did the ablation experiment not compare the results of removing CLIP generated pseudo labels? Have you considered the issue of using CLIP to add additional information leading to improved accuracy? Have you compared the related increased time consumption?\n\n**A1**: We have conducted the ablation experiment to evaluate the impact of removing CLIP generated pseudo labels, and the results are presented as VPDS w/ Pred and VPDS w/ Prot in Table 3 of the main text. Specifically, VPDS w/ Pred replaces CLIP assisted pseudo-labeling strategy with the prediction results of the model, VPDS w/ Prot leverages prototypes as the classifier for pseudo-label generation. The experimental results confirm that our CLIP assisted pseudo-labeling strategy outperforms conventional pseudo-labeling approaches.\n\nWe leverage CLIP to provide corrective information for pseudo-label generation, thus the use of CLIP facilitates improved performance. Notably, VPDS w/ Pred still outperforms all compared methods in Table 1 of the main text, demonstrating that our method is effective even without incorporating additional CLIP-derived information.\n\nWe compare the time consumption of VPDS with the two variants (VPDS w/ Pred and VPDS w/ Prot). Table 17 reports the time required for 10 epochs on three randomly selected tasks. We can observe that the increased time consumption of our method is not obvious.\n\n**Table 17**: Time consumption (s) of VPDS with several pseudo-labeling approaches.\n\n| Method | P$\\rightarrow$R | R$\\rightarrow$P | A$\\rightarrow$D | D$\\rightarrow$A |\n| ---- | ---- | ---- | ---- | ---- |\n| VPDS w/ Pred | 301.21 | 311.38 | 39.59 | 40.77 |\n| VPDS w/ Prot | 300.59 | 308.35 | 38.79 | 40.76 |\n| VPDS | 346.47 | 322.36 | 48.39 | 57.03 |\n\n\n**Q2**: The article emphasizes dual space decoupling, but Hamming space training relies on pseudo labels in the feature space. Is there any information leakage? What’s the essential difference between the coupled alignment (feature+hash joint optimization) of CPH and the decoupled alignment in this paper\n\n**A2**: Our method leverages the high-dimensional feature space to provide knowledge (i.e. pseudo-labels) for the low-dimensional Hamming space, resulting in a unidirectional constraint between the two spaces. In contrast, CPH enforces relation preservation across both domains, leading to a bidirectional constraint between the spaces. Since bidirectional constraints are stricter than unidirectional ones, our method effectively relaxes the rigid cross-space constraints.\n\nFurthermore, we argue that for effective hashing retrieval, it is sufficient to ensure that semantically similar samples are mapped to similar representations within each individual space, even if the feature structures of the two spaces differ. Therefore, we relax the requirement for consistent cross-space feature structures, instead adopting a decoupled approach that focuses on semantic learning within each space independently.\n\n\n**Q3**: The architecture and optimization objectives proposed in the article are quite similar to CLIP the Divergence: Language-guided Unsupervised Domain Adaptation, except for the addition of a hash encoder and classifier for the hash task. The related work is also not mentioned. Is there any reference?\n\n**A3**: Thank you for your comment, we will include this article as a related work. Although both methods utilize CLIP to provide auxiliary information for pseudo-labeling, our framework differs in several aspects from it, including the integration approach between the learned model and CLIP, the construction of text prompts, and other design details.\n\n\n**Q4**: Adding a comparison of CLIP's time consumption (especially compared to CPH, not only PEACE at the additional part).\n\n**A4**: Since CPH does not take raw samples as input, instead, its inputs are the feature matrices derived from image preprocessing. Consequently, CPH omits both the image encoding step and backbone fine-tuning step, resulting in obviously lower time consumption compared to PEACE and our method. \n\nFor a fair comparison, we use VGG16 as the feature encoder and apply CPH to the features encoded by VGG16. As shown in Table 18, when compared with the results in Table 6 of the appendix, we observe that the time consumption of our method is comparable to that of CPH+VGG16.\n\n**Table 18**: Time consumption of CPH+VGG16.\n\n| Tasks | A$\\rightarrow$R | R$\\rightarrow$A | C$\\rightarrow$R | R$\\rightarrow$C | P$\\rightarrow$R | R$\\rightarrow$P | A$\\rightarrow$D | A$\\rightarrow$W | D$\\rightarrow$A | D$\\rightarrow$W | W$\\rightarrow$A | W$\\rightarrow$D | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| Time (s) | 211.28 | 179.19 | 300.48 | 335.32 | 303.45 | 320.99\t | 38.73 | 54.33 | 39.81| 40.59 | 61.69 | 42.67 | 160.71 |"}, {"review_id": "rcFTPJkSU7", "reviewer": "Reviewer_CWRx", "summary": "This manuscript propose to use vision-language model as the pseudo-labeling tool to improve the performance of unsupervised domain adaptive hashing models. The idea of brining VLMs into UDA for improved visual understanding is quite interesting, however, there are some model components needs further clarification.", "questions": "- The proposed method seems to be workable in traditional UDA tasks such as classification on office-home and office-31? Can you provide the results on this task?\n- Please provide more details on why the VLM is not trainable?\n- It's a bit confusing that why we use VLM as a auxiliary corrective information, if the VLM can already outperform existing feature encoder, why not just use VLM.\n- How much additional memory is needed for storing the two additional memory bank?\n- ViT-B/16 has 86 million parameters, while feature encoder (VGG) is much smaller, is VGG really needed in the framework?\n- Larger dataset such as domain net might be needed as the size of VLM (ViT-B/16) is much larger then VGG or ResNet.\n- For Equation 10, why using this linear cofficient to integrate target predicted labels and the clip predicted labels? The assumption here seems to trust CLIP completely in the beginning, and when the model is trained, we can rely less on the CLIP output. If the computational cost of CLIP is a concern, can we design a smaller VLM and distill the larger CLIP model to it?\n- Do we know what's the zero shot CLIP model's performance on classification stage as suggested in Figure 1?", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Strengths\n  - The idea of bringing VLM to UDA is quite interesting and the results looks promising.\n  - The manuscript is well structured and technically sound.\n- Weaknesses\n  - The cost of additional components seems to be massive.\n  - Larger dataset might be needed due to a much larger backbone model is used.\n  - Need to discuss the motivation of keeping existing model structure (VGG) while VLM is performing much better.", "was_revised": true, "final_justification": "The authors has addressed my questions through rebuttal, I have increased my rating.", "rebuttal": "**To Reviewer CWRx**:\n\n**Q1**: The proposed method seems to be workable in traditional UDA tasks such as classification on office-home and office-31? Can you provide the results on this task?\n\n**A1**: The classification results on Office-Home and Office-31 are presented in Tables 19 and 20. While VPDS demonstrates competitive performance, it underperforms several recently proposed UDA classification methods. This divergence arises from the fundamental differences between the two tasks: classification requires precise assignment of each image to a category, whereas hashing retrieval prioritizes the ranking quality of similar images, tolerating partial sorting deviations. These distinct objectives require different solutions, which explains VPDS's superiority in retrieval tasks and relative suboptimality in classification.\n\n**Table 19**: Performance of VPDS in UDA classification on Office-Home.\n\n| Method | A$\\rightarrow$C | A$\\rightarrow$P | A$\\rightarrow$R | C$\\rightarrow$A | C$\\rightarrow$P | C$\\rightarrow$R | P$\\rightarrow$A | P$\\rightarrow$C | P$\\rightarrow$R | R$\\rightarrow$A | R$\\rightarrow$C | R$\\rightarrow$P | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| VPDS | 38.83 | 65.28 | 73.70 | 54.68 | 62.90 | 64.13 | 55.29 | 37.80 | 73.77 | 67.20 | 42.54 | 79.97 | 59.67 |\n\n**Table 20**: Performance of VPDS in UDA classification on Office-31.\n\n| Method | A$\\rightarrow$W | D$\\rightarrow$W | W$\\rightarrow$D | A$\\rightarrow$D | D$\\rightarrow$A | W$\\rightarrow$A | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | \n|VPDS | 88.43 | 96.48 | 97.79 | 89.16 | 68.58 | 65.42 | 84.31 |\n\n\n**Q2**: Please provide more details on why the VLM is not trainable?\n\n**A2**: Due to the large number of parameters in VLM, fine-tuning it incurs substantial time and memory consumption. To ensure efficient model training, we do not fine-tune the VLM itself and instead utilize its outputs directly.\n\nDuring the early training phase, the source domain model has not yet adapted to the target domain, thus the pseudo-labels generated by the model contain much noise. Given that many pre-trained VLMs exhibit strong generalization capabilities, we leverage them to provide auxiliary information, especially in the early training stage.　\n\n\n**Q3**: It's a bit confusing that why we use VLM as an auxiliary corrective information, if the VLM can already outperform existing feature encoder, why not just use VLM.\n\n**A3**: Although VLMs typically exhibit strong generalization capabilities, a model trained specifically for a domain can outperform VLMs by capturing domain-specific knowledge. During the early training phase, the pronounced domain shift results in the model performing poorly on the target domain. As training proceeds, the model accumulates more knowledge about the target domain, leading to a gradual performance improvement on that domain. Therefore, we use VLM as an auxiliary model without fine-tuning, and gradually decrease its contribution as the model gradually adapts to the target domain. \n\nIn addition, we conduct an experiment to compare using only the VLM (CLIP used in this paper) for generating pseudo-labels and our VPDS method. The results in Table 21 indicate that relying solely on the VLM leads to an obvious performance drop, demonstrating that our design of using VLM as an auxiliary corrective information, and gradually decreasing its contribution is reasonable.\n\n**Table 21**: Comparison of only VLM and VPDS on Office-31.\n\n| Method | A$\\rightarrow$W | D$\\rightarrow$W | W$\\rightarrow$D | A$\\rightarrow$D | D$\\rightarrow$A | W$\\rightarrow$A | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | \n| Only VLM | 51.28 | 36.84 | 35.07 | 67.77 | 39.10 | 76.31 | 51.06 |\n| VPDS | 87.22 | 91.40 | 65.88 | 98.91 | 64.88 | 100 | 84.72 |\n\n**Q4**: How much additional memory is needed for storing the two additional memory banks?\n\n**A4**: The memory banks $M_F^s$ and $M_F^t$ have dimensions of $|Y| * d_F$, where $|Y|$ denotes the number of classes and $d_F$ represents the dimension of feature representations encoded by the feature encoder. \n\nThe memory banks $M_H^s$ and $M_H^t$ have dimensions of $|Y| * d_H$, where $|Y|$ denotes the number of classes and $d_H$ represents the dimension of hash codes encoded by the hash encoder. \n\nThe memory consumption for storing these memory banks are presented in Table 22.\n\n**Table 22**: Memory consumption for storing memory banks.\n\n| Datasets | Office-Home | Office-31 | Digits |\n| ---- | ---- | ---- | ---- |\n| Memory consumption (MB) | 2.07 | 0.99 | 0.32 |\n\n\n**Q5**: ViT-B/16 has 86 million parameters, while feature encoder (VGG) is much smaller, is VGG really needed in the framework?\n\n**A5**: In practice, the feature encoder can be implemented by VGG, ResNet or Transformer. For fair comparison with baselines, we adopt VGG, as it is the architecture used consistently across all compared methods.\n\nRegarding ViT-B, we only utilize CLIP (which is based on ViT-B) to provide auxiliary information. Notably, the CLIP model is not fine-tuned in our method, resulting in manageable time and memory consumption.\n\n\n**Q6**: Larger dataset such as DomainNet might be needed.\n\n**A6**: We evaluate our method on the large-scale DomainNet dataset. Due to time constraints, we select 4 domains and construct 6 tasks for evaluation. The MAP results are presented in Table 23. \n\nDomainNet is widely recognized as one of the most challenging unsupervised domain adaptation datasets, its obvious domain shifts and large number of classes lead to substantially lower MAP results compared to the other datasets in the main text. Notably, our method outperforms the recently proposed PEACE, highlighting its robustness and effectiveness in handling complex large-scale scenarios.\n\n**Table 23**: MAP (%) of cross-domain retrieval tasks on DomainNet.\n\n| Method | rel$\\rightarrow$skt | rel$\\rightarrow$clp | rel$\\rightarrow$pnt | clp$\\rightarrow$rel | pnt$\\rightarrow$skt | skt$\\rightarrow$pnt | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |  \n| PEACE | 11.08 | 28.86 | 29.32 | 34.02 | 10.48 | 14.01 | 21.30 |\n| VPDS | 29.55 | 38.37 | 36.82 | 47.34 | 30.40 | 33.63 | 36.02 |\n\n\n**Q7**: For Equation 10, why using this linear coefficient to integrate target predicted labels and the clip predicted labels? If the computational cost of CLIP is a concern, can we design a smaller VLM and distill the larger CLIP model to it?\n\n**A7**: In the early training phase, the domain shift is pronounced, such that the model learned on the labeled source domain fails to generate reliable pseudo-labels for the unlabeled target domain. Given that the pre-trained VLMs exhibit strong generalization capabilities, which are typically more generalizable than the model learned on the source domain, we introduce VLMs to provide auxiliary knowledge for generating pseudo-labels, particularly during this early stage. As training progresses, the domain shift is reduced and the model captures more knowledge about the target domain, thus the learned model is more suited to the target domain than VLMs, consequently, the pseudo-labels generated by the model are more reliable. Therefore, we gradually increase the contribution of the learned model, while reducing that of the VLM.\n\nSince the linear coefficient is a simple approach for gradually improving the contribution of one component, we use it to adjust the relative contribution of the learned model and VLM. Notably, a gradually increasing coefficient remains effective. We design an alternative non-linear coefficient: $\\beta=1-exp(-\\epsilon e)$, where $\\epsilon$ controls the growth rate of $\\beta$. To evaluate this non-linear coefficient, we conduct experiments on Office-31. Results in Table 24 outperform those of compared methods in Table 1 of the main text, validating the effectiveness of this alternative gradually increasing coefficient.\n\n**Table 24**: Cross-domain retrieval performance of VPDS with $\\beta=1-exp(-\\epsilon e) $.\n\n| Method | A$\\rightarrow$D | A$\\rightarrow$W | D$\\rightarrow$A | D$\\rightarrow$W | W$\\rightarrow$A | W$\\rightarrow$D | Avg |\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | \n| VPDS ($\\epsilon=1$) | 84.27 | 84.45 | 67.95 | 98.87 | 63.47 | 99.93 | 83.16 |\n| VPDS ($\\epsilon=2$) | 85.05 | 88.46 | 69.68 | 98.90 | 63.65 | 97.39 | 83.86 |\n| VPDS ($\\epsilon=3$) | 85.20 | 83.25 | 69.38 | 98.93 | 63.39 | 99.10 | 83.21 |\n\nRegarding “distilling the larger CLIP model to a smaller VLM”, this is an interesting idea. However, we only leverage the strong generalization capabilities of publicly available pre-trained VLMs. Although distillation could reduce the computational cost of CLIP, it requires additional algorithmic design, which is an aspect beyond the scope of this work.\n\n\n**Q8**: Do we know what's the zero shot CLIP model's performance on classification stage?\n\n**A8**: We do not know the zero shot performance of CLIP in the classification stage. Two key observations guide our design. First, the model learned on a specific source domain generally exhibits weaker generalization capability than CLIP, which is learned on a large-scale dataset, thus we use CLIP to provide corrective information for pseudo-labels especially in the early training stage. Second, as training progresses, the learned model captures more knowledge of the target domain and gradually  outperforms CLIP on the target domain, thus we dynamically adjust their relative contributions. These two principles ensure that the learned model and CLIP work in synergy."}]

Author rebuttal:

Meta-review: This paper introduces the Vision-Language Model assisted Pseudo-Labeling and Dual Space Adaptation (VPDS) method, a novel and promising approach to addressing an important and practical problem. The proposed framework demonstrates both technical soundness and clear applicability, making it a valuable contribution to the field. 

Although the paper initially received mixed ratings, the authors provided a strong and convincing rebuttal that successfully clarified the methodology, addressed potential limitations, and reinforced the empirical evidence. As a result, all reviewers ultimately gave positive ratings. Overall, the paper is well-written, thoughtfully executed, and presents a meaningful advancement with strong potential impact.
Decision: Accept (poster)