Title: Domain Adaptive Hashing Retrieval via VLM Assisted Pseudo-Labeling and Dual Space Adaptation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Domain Adaptive Hashing

Image Retrieval

Pseudo-Labeling Strategy

Unsupervised Domain Adaptation
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n- 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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nQuality: 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).\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe 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).", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nPros.\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.\n\nQUESTIONS:\nSee above.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nPlease refer to the weaknesses, especially about the out-of-sample ability evaluation.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nNone", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}