Test-time Adaptation for Cross-modal Retrieval with Query Shift

Authors: Haobin Li, Peng Hu, Qianjun Zhang, Xi Peng, XitingLiu, Mouxing Yang
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: test-time adaptation, cross-modal retrieval, query shift

Abstract:
The success of most existing cross-modal retrieval methods heavily relies on the assumption that the given queries follow the same distribution of the source domain. 
However, such an assumption is easily violated in real-world scenarios due to the complexity and diversity of queries, thus leading to the query shift problem.
Specifically, query shift refers to the online query stream originating from the domain that follows a different distribution with the source one.
In this paper, we observe that query shift would not only diminish the uniformity (namely, within-modality scatter) of the query modality but also amplify the gap between query and gallery modalities. 
Based on the observations, we propose a novel method dubbed Test-time adaptation for Cross-modal Retrieval (TCR). 
In brief, TCR employs a novel module to refine the query predictions (namely, retrieval results of the query) and a joint objective to prevent query shift from disturbing the common space, thus achieving online adaptation for the cross-modal retrieval models with query shift.
Expensive experiments demonstrate the effectiveness of the proposed TCR against query shift. 
Code is available at https://github.com/XLearning-SCU/2025-ICLR-TCR.

Decision: accept (spotlight)
Reviewer scores: 6, 8, 8, 8

Reviews and discussion:
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creation date:
1730100745051

thread_type:
official_review

<thread_object>
creator: reviewer_jdwd

<content>
summary:
This paper introduces a novel method named TCR for addressing the query shift problem in cross-modal retrieval. TCR employs a test-time adaptation approach that leverages a multi-scale adaptive convolutional neural network and a hybrid transformer module to refine query predictions and adapt to shifts in query distribution without additional training data. The method is designed to enhance the uniformity of the query modality and reduce the gap between query and gallery modalities, thereby improving retrieval performance. The study demonstrates TCR's effectiveness on image-text retrieval tasks using standard benchmarks and various corruption types.

strengths:
1.	The paper proposes a novel test-time adaptation method (TCR) to address the query shift problem in cross-modal retrieval. This method achieves robustness against query shift by adjusting query predictions and designing a joint objective function, which is an interesting and potentially influential direction for research.
2.	The authors have conducted extensive experiments on multiple datasets, including COCO-C and Flickr-C, to verify the effectiveness of the proposed method. The experiments cover comparisons across different model types and sizes, as well as varying severity levels of query shift, demonstrating the robustness of the method.
3.	The paper not only introduces a new method but also provides an in-depth analysis of the impact of query shift on cross-modal retrieval, revealing how query shift can reduce the uniformity of the query modality and increase the gap between the query and gallery modalities. These theoretical analyses offer valuable insights for future research.

weaknesses:
1.	The TCR method proposed in the paper performs model adaptation at test time, which may increase additional computational costs. It is recommended that the authors analyze the computational complexity of the model and the additional cost incurred.
2.	Are the COCO-C and Flickr-C datasets constructed by the authors themselves? It seems that the paper does not explain whether the results of the baseline methods for comparison were obtained by the authors' own experiments or cited from their respective articles. If they were obtained through their own experiments, it should be clarified whether such comparisons are fair (whether they were trained on the new baselines), which is quite confusing for readers.
3.	The authors are advised to provide more explanation on the baselines and to include a few actual examples of query shifts to help readers intuitively feel the task.

questions:
Please address my concerns proposed in Weakness.
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<thread>
creation date:
1730182239257

thread_type:
official_review

<thread_object>
creator: reviewer_t1ei

<content>
summary:
This paper introduces a novel setting, cross-modal retrieval under query shift. To address this challenge, it introduces a test-time adaptation method called TCR, which includes a query prediction refinement module to produce retrieval-optimized predictions for incoming queries. Additionally, it employs a joint objective function for online adaptation, effectively handling the query shift and noise.

strengths:
1. The research question, cross-modal retrieval under query shift, is challenging and holds significant practical relevance.
2. Although this method builds on the principles of TTA, it also reveals TTA’s limitations in cross-modal retrieval and effectively overcomes these challenges.
3. Extensive experiments demonstrate the effectiveness of the proposed TCR method.
4. The paper is well-organized and well-written, enhancing the clarity and impact of its findings.

weaknesses:
1. This research setting is limited by the assumption that each query batch contains i.i.d. samples. However, in real scenarios, query shift may occur unpredictably, introducing non-i.i.d. data within the same batch. This raises concerns about the method’s applicability under such conditions.
2. Regarding the emergence of query shift, I am curious whether temporal issues, such as temporal shifts or concept drift discussed in [1-3], are present in real-world scenarios. Could the authors provide relevant discussion on this aspect?   
    [1] Evolving standardization for continual domain generalization over temporal drift. *NIPS 2023*.
    [2] Temporal domain generalization with drift-aware dynamic neural networks. *arXiv preprint arXiv:2205.10664* (2022)
    [3]Online Boosting Adaptive Learning under Concept Drift for Multistream Classification, AAAI 2024
3. In Section 3.2.1 on candidate selection, it would be valuable to address two points: first, whether gallery shift affects the outcomes of nearest neighbor selection; and second, how the number of selected candidates impacts the results. Additional experiments should be conducted to clarify these aspects.
4. In Section 3.2.2, given the shift between the source and target domains, it is unclear why source-domain-like data can be directly selected based on centers. Could the authors provide further analysis and explanation on this approach?
5. In section 3.5the definition of S(x_{i}^Q) in Equation (11) lacks corresponding theoretical analysis.
6. In the experiments, the authors employed various methods to generate image or text query shifts. I believe the results may depend on the specific shift generation techniques used. Therefore, it is crucial to provide access to the data processing methods and code to ensure the reproducibility of the experimental results.
7. I recommend that the authors discuss the limitations of the proposed method and outline specific future research directions. This would provide readers with additional insights and considerations for further exploration.

questions:
discussed in Weaknesses section.
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</thread>
<thread>
creation date:
1730656771783

thread_type:
official_review

<thread_object>
creator: reviewer_e6xm

<content>
summary:
The paper presents a Test-time adaptation for Cross-modal Retrieval (TCR) method to address query shift, which is a critical and understudied problem in cross-modal retrieval tasks. Query shift occurs when the distribution of online query streams differs from the source domain, leading to performance degradation in existing models. TCR introduces a query prediction refinement module and a joint objective function to refine query predictions and prevent query shift from disturbing the common space. It improves the existing test-time adaptation (TTA) methods with the capacity to manipulate both the modality uniformity and modality gap. Overall speaking, this paper is well-organized and of practical value.

strengths:
The proposed TCR method addresses an important problem and is supported by strong experimental results.

It provides extensive experiments demonstrating the effectiveness of TCR against query shift. The comparisons with existing TTA methods show convincing improvements, with is a strong validation of the ablation study .

weaknesses:
In Section 4.2, it is said that “We compare TCR with five SOTA TTA methods (Tent (Wang et al., 2021),  EATA(Niu et al.,2022), SAR(Niu et al.,2023), READ(Yang et al.,2024), and DeYO...”. These methods should be introduced in Section 2.2 of the related work part. 

Line 212, ,where Q and G denotes as query modality and gallery modality for clarity in the following. Change to “denote””

Tables 1 and 2 appear too early. They should not be on Page 7 but on the page where they are referred for the first time,

questions:
see above comment
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</thread>
<thread>
creation date:
1730805665239

thread_type:
official_review

<thread_object>
creator: reviewer_wks8

<content>
summary:
The paper addresses the challenge of cross-modal retrieval in scenarios where the query data distribution deviates from the source domain, a phenomenon known as "query shift." This deviation often leads to a performance decline in cross-modal retrieval systems. The authors propose a novel approach called TCR: Test-time adaptation for Cross-modal Retrieval, which adapts cross-modal retrieval models during inference to account for query shift. The proposed method includes a query prediction refinement module and a joint objective function to prevent the disturbances caused by the query shift, enhancing the uniformity within the query modality and minimizing the gap between query and gallery modalities. The model is designed to operate effectively in real time by adapting to changing online queries. The approach was tested on six popular image-text datasets and demonstrated superior performance against existing test-time adaptation (TTA) techniques.

strengths:
1) The paper tackles the underexplored problem of query shift in cross-modal retrieval, providing a comprehensive analysis of its effects on retrieval performance. The method's unique combination of query prediction refinement and multiple loss functions sets it apart from traditional TTA approaches.
2) The authors conducted extensive experiments across six datasets and compared their method against several state-of-the-art TTA models. The amount of experiments is fair and convincing. 
3) The paper proposes a joint objective consisting of three loss functions—uniformity learning, gap minimization, and noise-robust adaptation—that each address specific challenges introduced by query shift. This is a novel design for this problem.

weaknesses:
1)  The authors provide only limited discussion regarding the sensitivity of the various hyperparameters involved, such as the temperature and trade-off parameters. A more detailed analysis would improve understanding of the model's adaptability to different scenarios.
2) The approach heavily relies on pre-trained models and assumes the existence of a well-aligned common space. In cases where the source domain model lacks robust representations, the effectiveness of TCR may be diminished. This could limit the generalizability of the approach to pre-trained models of different quality. More discussions and insights need to be given in order to make the paper more readable.

questions:
1) The proposed TCR method aims to enhance retrieval robustness under query shift by manipulating modality uniformity and the modality gap. Given the variety of potential shifts in real-world data (e.g., subtle cultural variations, extreme distortions, rare domain-specific content), how does TCR perform across these different types of shifts? 

2) Could the model's performance degrade if it encounters shifts it was not explicitly evaluated against? A thorough breakdown of the model’s robustness to a diverse set of query shifts would strengthen the understanding of its general applicability.

3) This paper introduces several hyperparameters, such as the temperature parameter (τ) for controlling the trade-off between smoothness and sharpness, and others for balancing the different loss terms. How sensitive is TCR to these hyperparameters, and how easy is it to tune them for new domains? Some more results from these ablations studies will be very beneficial.
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Metareview:
metareview:
This paper tackles the challenging 'query shift' problem in cross-modal retrieval, in which the distribution of query data deviates from the source domain. They formulate their algorithm as a test-time adaptation for cross-modal retrieval, which includes a query prediction refinement module to refine the query predictions and a joint objective function to prevent the disturbances caused by the query shift. Sufficient experimental results on multiple datasets show the effectiveness of the proposed method in such different cases. 

In the first round, the main concerns lie in the types of query shifts, robustness to a diverse set of query shifts, parameters, and other minor issues. After rebuttal, all these concerns were eliminated, and all the reviewers voted to accept this work.

After carefully reading the main document and supplementary docs, the AC has the following comments about this paper:  
***Advantages***  
This paper addresses the query shift problem in cross-modal retrieval during test time. The problem itself is practical due to the diverse and unpredictable nature of open-world queries. Through extensive experimental analyses, the authors identify two key challenges posed by query shift and propose an effective method to address them. The manuscript and appendix include thorough empirical studies validating the significance of the addressed problem and the effectiveness of the proposed approach.  
***Future Direction***  
Although the authors pointed out the query shift issue in cross-modal retrieval, how to effectively handle more potential scenarios contaminated with query shift should be further discussed and highlighted.  

In summary, due to novel problem definition, technical context and experimental results, this paper is a good work to be presented in ICLR 2025.