Neighbor-aware Contrastive Disambiguation for Cross-Modal Hashing with Redundant Annotations
Authors: Chao Su
Likang Peng
Yuan Sun
Dezhong Peng
Xi Peng
Xu Wang
Venue: NeurIPS 2025 spotlight ()
Keywords: Multimodal Learning
Cross-Modal Retrieval
Primary area: deep_learning
TL;DR: This paper proposes a novel method termed Neighbor-aware Contrastive Disambiguation (NACD) for Cross-Modal Hashing with Redundant Annotations, which effectively addresses the issue of annotation ambiguity and modality gap.

Abstract: Cross-modal hashing aims to efficiently retrieve information across different modalities by mapping data into compact hash codes. However, most existing methods assume access to fully accurate supervision, which rarely holds in real-world scenarios. In fact, annotations are often redundant, i.e., each sample is associated with a set of candidate labels that includes both ground-truth labels and redundant noisy labels. Treating all annotated labels as equally valid introduces two critical issues: (1) the sparse presence of true labels within the label set is not explicitly addressed, leading to overfitting on redundant noisy annotations; (2) redundant noisy labels induce spurious similarities that distort semantic alignment across modalities and degrade the quality of the hash space. To address these challenges, we propose that effective cross-modal hashing requires explicitly identifying and leveraging the true label subset within all candidate annotations. Based on this insight, we present Neighbor-aware Contrastive Disambiguation (NACD), a novel framework designed for robust learning under redundant supervision. NACD consists of two key components. The first, Neighbor-aware Confidence Reconstruction (NACR), refines label confidence by aggregating information from cross-modal neighbors to distinguish true labels from redundant noisy ones. The second, Class-aware Robust Contrastive Hashing (CRCH), constructs reliable positive and negative pairs based on label confidence scores, thereby significantly enhancing robustness against noisy supervision. Moreover, to effectively reduce the quantization error, we incorporate a quantization loss that enforces binary constraints on the learned hash representations. Extensive experiments conducted on three large-scale multimodal benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches, thereby establishing a new standard for cross-modal hashing with redundant annotations. Code is available at https://github.com/Rose-bud/NACD.

Neighbor-aware Contrastive Disambiguation for Cross-Modal Hashing with Redundant Annotations
Chao Su Likang Peng Yuan Sun 2, Dezhong Peng 1.3 Xi Peng 1.2 Xu Wang 1,4+ CColleee of Computer Science, Sichuan University, Chengdu, China NNational Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, Chengdu, China TTanfu Jincheng Laboratory, Chengdu, China
*Centre for Frontier AT Research (CFAR), A*SAA Singapore suchao @gmail com, likangpeng@stu. scu, edu. en, sunyuan_work0163. com, pengdzăscu. edw.eedscueee..c.. pengx. gm@gmail com, wangxu Sangxu.scuQqmaiie
Abstract
Cross-modal hashing aims efficiently retrieve information across different modal- ities by mapping data into compact hash codes. However, most existing methods assume access fully accurate supervision, which rarely holds in real-world sce- narios. In fact, annotations are often redundant, i.e., each sample is associated with set of candidate labels that includes both ground-truth labels and redundant noisy labels. Treating all annotated labels as equally valid introduces two critical issues: (1) the sparse presence of labels within the label set
explicitly addressed, leading to overfitting on redundant noisy annotations; (2) redundant noisy labels induce spurious similarities that distort semantic alignment across modalities and propose degrade the quality of the hash space. To address these challenges, that effective cross-modal hashing requires explicitly identifying and leveraging the true label subset within all candidate annotations. Based on this insight, we present Neighbor-awarr Contrastive Disambiguation (NACD), novel framework designed for robust learning under redundant supervision. NACD consists of two key components. The first, Neighbor-awer aware Confidence Reconstruction (NACR), refines label confidence by aggregating information from cross-modal neighbors distinguish true labels from redundant noisy ones. The second, Class-aware Robust Contrastive Hashing (CRCH), constructs reliable positive and negative pairs based on label confidence scores, thereby significantly enhancing robustness against noisy supervision. Moreover, effectively reduce the quantization error, we in- corporate a quantization loss that enforces binary constraints on the learned hash representations. Extensive experiments conducted on three large-scale multimodal benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches, thereby establishing new standard for cross modal hashing with redundant annotations. Code is available at http:://ithub..oo/Ros-bbbbbbb
Introduction
With the explosion of large-scale and diverse data on the Internet (177), efficiently retrieving seman- tically relevant data across modalities has become increasingly important [6]. For large-scale datasets, cross-modal hashing (CMH) offers effective solution by encoding heterogeneous data into compact binary hash codes, enabling high retrieval efficiency and low storage cost. The core
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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challenge of CMH lies in effectively leveraging available supervision while minimizing semantic discrepancies between different modalities.
Existing CMH methods can be broadly categorized into unsupervised and supervised approaches based whether the label information is available. The unsupervised CMH methods [17 22
learn hash functions by exploring the intrinsic structure and similarity of the data without access to labels. For instance, CIRH [21] jointly preserves the multimodal correlation and identity semantics into binary hash codes based on a heterogeneous graph network. Moreover, UCCH [22] proposes contrastive learning-bas -based unsupervised CMH method with a momentum optimizer and cross- modal ranking learning loss to improve performance. However, the lack of supervision limits their ability learn semantically discriminative representations. In contrast, supervised methods [13 leverage label information to learn more discriminative hash codes to improve retrieval performance.
Within a probabílistic modality alignment framework, MIAN [27 investigates the preservation of asymmetric similarities both within and between modalities, thereby fully utilizing multi-level
semantic information throughout the entire database. RSHNL 33 designs Robust Self Self-paced Hashing mechanism that mitigates the misleading effects of noisy labels on the model by simulating the human cognitive process. While these supervised methods have achieved satisfactory retrieval performance by leveraging label information, they implicitly rely two assumptions: (1) all labels in the training data are accurate; (2) noisy annotations are simulated by replacing correct labels with incorrect ones. However, in real- world applications, data annotations are often redundant, i.c., each instance is labeled with a candidate label set that includes both true and spurious labels. We refer to this setting redundant annotations. As shown in Fig. L among the annotations of the anchor sample pair, only "Sea, Plant, Beach, Cloud" are correct labels, while the others are additional noisy labels. Such redundancy severely distort semantic similarity estimation and hinder effective hash learning by introducing spurious correlations across modalities. Crucially, existing methods fail to explicitly distinguish true labels within the candidate label set, leading degraded performance under redundant noisy supervision. Although partial multi-label learning (PML) (32H35] provides potential solution for redundant annotations, most PML methods assume a shared feature space and overlook cross cross-modal semantic divergence. In contrast, CMH must address both accurate label disambiguation and modality-rolee -robust contrastive pair construction, which remains a rarely explored challenge.
A nchor
Neighbor
Redundant A nnotations
Redundant A anotations
Sea
Car
Sea
Horse
Mountain
Beach
Airplane
Boat
Plant
Cloud
P
Plant
Bridge
serene beach scene with footprints the sand, calm waters, and plants under partly cloudy sky.
coastal town with bustling harbor filled with boats, surounded by plant and colortul buildings.
Road
Human
Building
Sky
Figure I:
The redundant annotations in cross-modal hashing. Among the annotations, the correct labels and redundant noisy labels are represented by green and red, respectively. Shared labels between the anchor sample pair and its neighbor sample pair are enclosed dashed boxes.
In this paper, we focus on the practical scenario of redundant annotations and propose a novel framework, Neighbor- Contrastive Disambiguation (NACD), for robust cross-modal hashing. NACD benefits from an efficient Neighbor-awarr Confidence Reconstruction (NACR) module and novel Class-aware Robust Contrastive Hashing (CRCH) module. Specifically, NACR integrates sample label confidence with its cross-modal neighborhood label confidence to identify the ground- truth labels within the entire annotations. CRCH dynamically constructs positive and negative sample pairs based on class confidence thresholds, reducing erroneous associations caused by misleading labels and significantly enhancing the model's robustness to redundant supervision. Moreover, to effectively reduce the quantization error, we employ an effective quantization loss that enforces binary constraints on the learned hash representations. The main contributions of the proposed NACD are as follows:

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We first focus on the practical scenario of redundant annotations in cross moosll hashing and propose a novel Neighbor-awand Contrastive Disambiguation (NACD) framework that addresses the challenges caused by redundant noisy supervision.
To achieve accurate label disambiguation, efficient Neighbor- aware Confidence Recon- struction (NACR) module is presented, which integrates the sample pair with that aggregated from its cross-modal neighbors to identify the ground-truth labels within the entire annotations.
label confidence of the anchor
We design an innovative Class-aware Robust Contrastive Hashing (CRCH) module which dynamically constructs positive and negative sample pairs based on class- dass-wise confidence thresholds, reducing erroncous associations caused by redundant noisy labels and signifi- cantly enhancing the model's robustness to incorrect supervision.
Comprehensive experiments on three multimodal datasets, i.e., MIRFlickr-25k, NUS-WIDE, and MS-COCO, demonstrate that NACD consistently outperforms state-of-the-arr CMH methods across various redundancy levels.
Related Work
2.1 Cross-Modal Hashing
Cross- Cross-modal modall hashing aims to retrieve semantically relevant data across different modalities within a shared Hamming space. The key challenge lies in bridging the modality gap. To address this challenge, numerous approaches have been proposed, which be broadly divided into two cate- gories: unsupervised CMH methods and supervised CMH methods. More specifically, unsupervised learn modality-specifie transformations by maximizing cross-modal cor- relations without label supervision. For example, UCCH [22] integrates contrastive learning into unsupervised CMH to enhance retrieval performance and robustness. However, these unsupervised methods suffer from limited performance due to the absence of explicit supervision. Supervised CMH methods (29 36 are typically based on two assumptions: (1) all labels in the training data are accurate; (2) noisy annotations are simulated by replacing correct labels with incorrect ones. For example, HCCH 366 introduces a coarse-to caarse-to-fine to-fine hierarchical hashing strategy effectively utilize hierarchical features and accurate labels across modalities. RSHNI. (31] proposes robust self-paced hashing mechanism that emulates human cognition, thereby reducing the negative impact of noisy labels and improving model performance.
CMH methods [19 22
However, in real-world applications, multimodal annotations often contain "redundant annotations". Therefore, this paper focuses on a largely unexplored yet challenging problem: cross-modal hashing with redundant annotations.
2.2 Learning with Redundant Annotations
Partial multi-label learning (PML) trains models using redundantly annotated data, where each instance is associated with candidate label set containing both true and redundant noisy labels. The key challenge in PML lies in filtering out incorrect labels and identifying reliable ones, thereby recovering the true label distribution for supervision. To achieve this, a number of methods have been developed. These methods can be broadly categorized into smoothness assumption bbase,,, low- rank constraint- bonstraint-based, and sparsity regularization- based approaches. Smoothness assumption-baat approaches [35] 37 38) are based on the assumption that neighboring samples in the feature space more likely to have similar labels. The low-rank constraint- based approaches [39]4]] leverage the low- rank property to achieve disambiguation. The sparsity regularization-biet based approaches 32 33 40)
impose sparsity on the candidate label set, effectively suppressing noisy labels and facilitating
disambiguation.
In contrast to the aforemeenioned PML methods, our approach integrates the label confidence of an anchor sample pair with that aggregated from its cross-modal neighbors to identify true labels within all annotations. Furthermore, a dynamically updated class-wise threshold enables more accurate and adaptive label disambiguation.
3

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Image Bankbone
Leuannt
Classifier
Lerch Corésdence
Upaer
Labet Similarity
Fosture Similarity
See.
n.a
(-1.0)
Plant
0.8
Reed
0.1
Archer
(0-2,13
Beach
0.6
Reoostmackd Corldloss
Hash Code
Read
Classanaare Robast Contrastive Hastass Hashing
Naighlo--avaree Confidenee Reconstratiion
Lercn
Unger
Test B Backbone
Classifier
poa nomeeee
Lquant
Test Ingut
Figure 2: The pipeline of the proposed framework NACD for cross-modal hashing with redundant annotations. NACR refines label confidence by aggregating information from cross-modal neighbors to distinguish true labels from redundant noisy ones. Meanwhile, CRCH constructs reliable positive and negative pairs based the learned label confidence, which significantly improves robustness against noisy supervision.
3 Proposed Approach
3.1 Problem Definition
For ease presentation, we first give some definitions of cross-modal hashing of redundant annota-
tions. Suppose the input space is denoted as X' and the label space as ) 21,2.... K}, where K
indicates the total number classes. Denote {(
as the training set with N sample [[rr)] m Hb j=1
pairs, where 2""" represents the j-th instance from the m-th modality (m = 11 for image and m = 2 for text). y/ {0, 1 denotes the candidate label vector of the j-th sample pair, which encodes both ground-truth and redundant noisy labels. The k-th element of yj equals sample pair is annotated as class k. Here, Pj is the label confidence vector of the candidate label
the corresponding
vector Vy. The hash codes are denoted as {byr j=1 E {-1, where L is the hash code length. CMH leverages hash functions map data from different modalities into common Hamming space, enabling efficient similarity search across modalities through compact binary codes. Let the hash functions be /' where {1, 23. Due the NP-hard problem in binary optimization, calculate the hash representations by hm = tanh([mm (xx²²)
)), ), m E [1,2} in the training process. Thus, the final binary hash codes are obtained by applying the sign function: bTT sign(h)") ), E {1, 2}. Additionally, a linear classifier with a sigmoid activation function g(.) is employed to obtain the probability distribution 27t (h,"), where e [1,2].
3.2 Neighbor-aware Confidence Reconstruction
Under redundant supervision, confidence estimates obtained from individual samples may be unreli- able. Meanwhile, semantically similar samples across modalities often share the same true labels. Motivated by this observation, we present confidence-mixtun (CM) strategy reconstruct label confidence by balancing neighborhood consensus and self-prediction.
First, assume that the prediction probability of the j-th target sample {>:;aa m=1 is žj. under the supervision of redundant annotations Yj- We define the confidence of each sample's 's prediction by the model as:
Pj + 7D, + +1 -
7)
Mij
(I)
m=

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where o denotes Hadamard product, and y is momentum parameter that decays from 0.95 to 0.8 during training.
Since model predictions may be unreliable under redundant supervision, and similar samples tend to share the same true labels, exploit the predictions of cross-modal neighbors refine the confidence estimation. Given the C' nearest neighbors NV of the anchor sample pair 2j, the neighbor-aggregatte confidence qj is calculated as:
ECEN, Pe 4j EdEN, 8je
where 8jc- = (5) + +30))
where Sie denotes the fused similarity between the anchor j and its neighbor c. obtained by averaging the similarities from the image and text modalities. To mitigate the issue that inaccurate estimates during training may hinder model optimization, we reconstruct the anchor confidence Py by mixing with the neighbor-aggregate confidence d5- The reconstructed confidence is computed as:
+ >a + (1 - 3)p
(3)
where À is a mixture coefficient that balances neighborhood consensus and self self-prediction. After obtaining reconstructed confidence by Eq. (3), the disambiguation loss Lnner can be formulated as:
N
Lnner
[PJa log (zjh) + (1 P3s) log (1 =,hh]]
(4)
2N
where =5t denotes the predicted probability that the j-th sample belongs the k-th class under modality m, and Pjk represents the reconstructed confidence of the j-th sample pair for class k.
Empirically, incorporating neighborhood consensus into label confidence estimation improves disam- biguation accuracy, especially with abundant redundant noisy annotations. It enables the model to construct semantically faithful contrastive pairs, thus significantly enhancing the model's robustness.
3.3 Class-aware Robust Contrastive Hashing
Although NACR reconstructs label confidence effectively, it does not explicitly incorporate class- level information when constructing robust positive and negative pairs. Therefore, we propose Class-aware Robust Contrastive Hashing (CRCH) module to adaptively build reliable pseudo-labels thereby enhancing the stability of contrastive optimization.
First, we establish a class-wise threshold for each class based on the reconstructed label confidence. The threshold 44 for the k-th class is calculated as follows:
t
Pjk-
(5)
N
where Руλ denotes the reconstructed label confidence of the j-th sample pair on class k and Na indicates the number of samples for which Рÿı: O among all N samples. This class-specific averaging adaptively captures the distributional characteristics of each class rather than relying on fixed threshold. To build class-wise supervision, we derive the pseudo-label úū [ūjt,... [55]] by comparing each reconstructed confidence Pÿk. with its corresponding class-wise threshold 4.:
1, if Pyk 2 ty j5k
0, otherwise
where 2uą E {0, 1} indicates whether class considered positive for the j-th sample pair. For mini-batch containing n sample pairs, we compute a label similarity matrix [0, 1|"X" based on the interseetion-over-ume (IoU) between pseudo-label vectors:
Tú =
0, U
where Tú measures the semantic similarity between the i-th and j-th sample pair based on their pscudo-labels. A higher Ti indicates stronger semantic consistency, while Ti 0 means that
5

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the sample pairs are semantically disjoint. Accordingly, positive and negative pairs are determined by the indicator functions i 0] and , OJ. respectively. However, directly relying on Ti for pair construction can be unreliable due to redundant noisy annotations. To mitigate this issue, we follow [22] and introduce margin-based thresholding mechanism that adaptively adjusts cross-modal similarities matrix S to reduce the impact of negative pairs with overly high similarities. Specifically, the adjusted similarity matrix Nig is defined as:
S
St 25 S 5. otherwise
N
(8)
where {12,21} denotes image-to-text and text-to-image retrieval directions. S h hhj. ft h2 The margin parameter å distinguishes hard and easy negative pairs, while the shift parameter & suppresses the influence of overly easy negatives. The loss of CRCH is defined as:
Lerch- n
[T. = 0J Σ exp (N2) + I[T > 0] exp( Tzy)
1
S.t.
j=
(9)
where ferch consists of three components: (1) [Ti 0) E' exp(NN) penalizes all negative cross-modal sample pairs, especially those that are easily confusable. (2) [[T;; > 0] exp(-S,; -T₃) treats cross-modal sample pairs with non-zero label similarity Tiy as positive pairs and encourages greater semantic alignment. The higher the label similarity, the stronger the penalty imposed for insufficient feature similarity Stj. (3) Σ Sn encourages consistency within each sample pair by increasing the similarity between the image and text representations.
The CRCH module dynamically constructs positive and negative sample pairs based on class-wise confidence thresholds, enabling the model effectively capture nuanced relationships between samples. This design significantly reduces erroneous associations caused by redundant noisy labels and enhances the model's robustness to incorrect supervision.
3.4 Optimization
Benefiting from the NACR and CRCH modules, our model effectively learns discriminative hash codes within the Hamming space. However, the discrepancy between continuous representations and discrete binary codes inevitably leads to quantization errors, which can substantially degrade retrieval performance in CMH. To address this issue, we present an effective quantization loss as follows:
2
L
Equant n
hn sign(h3))..
(10)
where h3 denotes the 1-th element of the hash representation hT This loss penalizes the deviation between continuous hash values and their corresponding binary codes sign hT)) thereby encouraging each element to approach 1 and effectively reducing the quantization error, as demonstrated in our ablation studies. Thus, the final loss function NACD can be defined as:
[ Lneer + @Lerch BLquant:
(11)
where and hyperparameters that balance the contributions of Leaer, Lerchs and Lquantt Additional optimization details of NACD are provided Appendix Sec.
Experiments
4.1 Experimental Settings
To evaluate the effectiveness of NACD, we conducted experiments on three benchmark datasets:
MIRFlickr-25k (Flickr) [42), NUS-WIDE (NUS) [43, and MS-COCO (COCO) (44. For all methods, we evaluate the Mean Average Precision (MAP) on both Image-to-Tex (12T) and Text-to-Image (T2I) retrieval tasks. Note that all MAP scores are computed over the entire retrieval set (i.e., MAP@ ALL.). Additionally, precision-recall curves under the hash lookup protocol are employed to visually assess

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the performance of CMH. To distinguish between different levels of redundant annotations, we define a redundant rate, which represents the ratio between the number of redundant noisy labels and the number of ground-truth labels in the entire annotations. Experiments were conducted with hash code lengths of 32, 64, and 128 bits, under redundant rates of 1.0, 1.5, 2.0, and 2.5. Additional details about the datasets are provided in Appendix Sec. B
4.2 Implementation Details
In the proposed NACD, the image modality adopts the VGG19 model 45 as its CNN backbone. For text processing, the pre-trained Doc2 model 46 is employed as the backbone. For cross-modal shared representation learning, the image and text modalities employ three and two hidden layers, respectively. Each fully connected (FC) layer is succeeded by a ReLU activation layer, except for the final layer, which uses a tanh function. Each hidden layer contains 8, .192 units, followed by an output layer of dimension representing the shared embedding space. The model is trained using the RMSprop optimizer 47,, with an initial learning rate of 1e 5 and a maximum of 100 epochs. The parameters 6 and E in Eq. ( are set to 0.2 and 1.0, respectively. Additionally, employ a batch size of 128. The model is evaluated every 20 epochs, with the first 10 epochs serving as a warm-up phase during which the CM strategy and class-wise threshold update disabled. The number of neighbors C is set 20 to ensure accurate neighborhood information. Our NACD is implemented using the PyTorch framework [8 and all experiments are carried out with NVIDIA V100 GPUs.
pre-trained on ImageNet,
4.3 Comparison with State-of-the-Arts
In this work, we compare our NACD against 11 state-of-the-art CMH methods, including five
unsupervised methods: DISRH [8]. DGCPN (20) PIP (4), CIRH (21) and UCCH [22]; and
six supervised methods: CMMQ 50
MIAN (27]. LICMH 28 DHRL [29], NRCH 30), and
RSHNL [3I. The average MAP scores for the 12T and T2I tasks are reported in Table Additionally,
the experiment results with 8 and 16 bits can be found in Appendix Sec. Sec. Fig. Fig. presents precision-
recall curves on three datasets for hash code length of 128 bits and redundant rate of 2.5. Based on these results, we make the following observations:
As the hash code length increases, the performance of almost all methods improves, since longer codes contain more discriminative information in the Hamming space.
As the redundant rate increases, the performance of all supervised CMH methods deteri- orates, since the progressively redundant noisy supervision misleads model training. In contrast, NACD maintains stable and superior performance by effectively extracting correct supervision from redundant annotations. Meanwhile, unsupervised CMH methods remain unaffected as they do not rely on label information.
From Table [ we see that the proposed NACD consistently outperforms all competing methods across all settings. For instance, when the hash code length is 128 and the redundant rate is 2.5, NACD exceeds the second-best methods by 3.3%, 1.7%, and 2.6% the Flickr, NUS, and COCO datasets, respectively.
As illustrated in Fig. 3 the area under the peeail curves indicates that NACD consistently outperforms all other state-of-the-ar methods in both 12T and T2I tasks, demon- strating its stable and superior performance.
4.4 Ablation Study
To verify the effectiveness of each component in NACD, we conducted extensive ablation studies on the NUS and COCO datasets with hash code length of 128 bits across various redundant rates. We compared the full NACD with six ablated variants: (1) only Cnaer; (2) only Lereh: (3) NACD without the CM strategy: (4) NACD without the class-wise threshold ta fixed at 0.5; (5) NACD without Legaansi (6) NACD without the warm-up phase; and (7) the full NACD, As shown Table NACR, CRCH, and Lquant all effectively enhance the performance of NACD. Additionally, the warm-up strategy, the CM strategy, and dynamic wlass-wise threshold updating are all crucial for achieving optimal model performance.
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W630
3550
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20CPV
gredtson
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CHNO
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MRCH
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07
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(a) Flickr (12T)
(b) NUS (I2T)
COCO (12T)
0.857
MACO
0.80
0.7
3559
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2101
0.70
UCC1
0.60
0.4
MTCH
0.0
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0'4
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1.0
8.00
0.2
0.5
0.8
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(d) Flickr (T21)
(e) NUS (T21)
COCO (T2I)
Figure 3: The precision-recall curves on three datasets. Note that the hash code length is 128bits and the redundant rate is 2.5.
4.5 Parameter Analysis
To evaluate the impact of the coefficient À in Eq. (3), well as and 3 in Eq. (11) we conducted extensive experiments on the COCO dataset with a hash code length of 128 bits under various redundant rates. As shown in Fig. 4 X yields optimal results within the range of [0.01, 0.04], demonstrating the effectiveness NACR. Moreover, the model achieves superior performance when lies within [0.1, 0.5] and 3 within [0.5, 1.5), further validating the effectiveness of both CRCH and the quantization loss Lquent Additional parameter analyses on the Flick and NUS datasets are provided in Appendix Sec. C.2
0.70
0.70
0.70
0.65
0.68
MAP
MAP
0.60
0.67
Redundant Rate:1.0
Redundant Rate:1.0
Redundant
0.55
0.66
Redundant Rate:1.5
Redundant Rate:2.0
Redundant Rate:2.0
Redundant Rate:2.0
Redundant Rate:2.5
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0.01 0.02 0.03 0.04 0.05 0.08
0.50
0.1 0.3
0.5
1.5
0.65
0.3
B
(a) on COCO.
(b) a on COCO.
(c) 8 on COCO.
Figure 4: The performance of NACD in terms of average MAP scores versus different values of X, a. and 3 COCO dataset with 128 bits.
4.6 Model Analysis
Robustness Analysis. To intuitively demonstrate the robustness of NACD, we compared with two of variants and the NRCH method. Their average MAP scores on the COCO dataset were plotted under the settings of hash code length of 128 bits and redundant rates of 2.0 and 2.5. Specifically, NACD-1 denotes the variant without the CM strategy, while NACD-2 represents the variant which the class-win wise threshold & is fixed 0.5, meaning that dynamic threshold update is performed.

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Table 1: The performance comparison in terms of average MAP scores (%) of 12T and T2I tasks under different redundant rates and various bit lengths on the MIRFlickr-25K(Plickk NUS- WIDE(NUS), and MS-CCCO(COCO) datasets. The highest and second highest MAP scores among all methods are shown in bold and underline respectively.
1.0
1.5
2.5
Dataset
Method
Year
32hits
64bits
128bits
32bits
64hits
128hits
32hits
64bits
128bits
32hits
64bits
18hhits
DISRH
2019
63.4
64.8
66.5
63.4
64.8
66.5
63.4
64.8
66.5
63.4
64.8
66.5
DGCPN
2021
69.2
70.5
71.1
69.2
70.5
71.1
69.2
70.5
71.1
69.2
70.5
71.1
PIP
2021
67.9
68.9
69.8
67.9
68.9
69.8
67.9
68.9
69.8
67.9
68.9
69.8
CIRH
2022
68.6
69.4
70.1
68.6
70.1
69.4
70.1
68.6
69.4
70.1
UCCH
2023
71.0
71.9
72.0
71.0
72.0
71.0
71.2
72.0
71.0
71.2
72.0
Flickr
CMMQ
2022
64.1
65.5
66.5
63.9
65.6
63.6
63.8
64.8
MIAN
2023
71.8
73.0
67.6
62.8
63.2
61.6
61.3
60.2
LICMH
2023
63.2
64.3
65.3
60.0
59.8
60.6
57.8
58.5
56,9
56.4
57.6
DHRI.
2024
69.5
69.4
69.3
68.8
69.0
68.6
69.2
68.9
68.9
69.0
68.8
68.4
NRCH
2024
72.9
74.5
24.4
67.9
70.9
69.5
63.0
63.6
65.1
57.7
58.2
63.9
RSHNL
2025
71.8
72.2
72,5
69.2
69.5
70.5
66.5
67.5
67.9
64.4
65.0
66.0
NACD
Ours
76.5
76.8
77.1
76.4
76.8
76.9
75.7
76.2
76.3
74.3
74.6
75.3
DISRH
2019
46.7
49.5
52.1
46.7
49.5
52.1
46.7
49.5
52.1
46,7
49.5
52.1
DGCPN
2021
60.2
62.4
64.0
60,2
62.4
64.0
60.2
62.4
64.0
60,2
62.4
64.0
PIP
2021
57.3
59.1
59.9
57.3
59.1
59.9
$7.3
59.1
59.9
57.3
59.1
59.9
CIRH
2022
57.2
59.3
60.4
57.2
59.3
60.4
57.2
59.3
60.4
57.2
59.3
60.4
UCCH
2023
62.3
63.4
63.9
62.3
63.4
63.9
62.3
63.4
63.9
62.3
63.4
63.9
NUS
CMMQ
2022
$7.7
57.7
58.3
52.5
52.5
$2.7
44.8
44.2
44,1
36,9
36.4
36.8
MIAN
2023
63.5
63.6
64.1
58.9
60.2
61.2
$7.4
58.1
59.2
57.1
56.5
58.6
LICH
2023
$7.0
58.0
59.4
51.7
$2.5
53.2
50.6
49.5
49.5
45.3
45.5
46.6
DHRL
2024
61.0
61.0
60.8
60.3
60.2
58.9
59.2
57.8
57.7
58.7
57.4
NRCH
2024
68.6
69.3
67.4
67.7
68.0
66.6
67.3
67.6
RSHNL
2025
59.3
59.7
60.3
57.8
57.7
57.5
55.5
55.5
55.7
54.4
54.0
53.0
NACD
Ours
68.2
69.4
70.3
68.1
69.4
70.2
67.9
69.0
69.6
67.4
68.7
69.3
DISRH
2019
51.6
54.1
57.0
51.6
54.1
57.0
51.6
54.1
57.0
51.6
54.1
57.0
DGCPN
2021
63.5
64.3
63.0
63.5
63.0
63.5
64.3
PIP
2021
55.7
57.8
58.2
55.7
57.8
58.2
55.7
57.8
58.2
55.7
57.8
58.2
CIRH
2022
63.0
63.5
64.2
63.0
63.5
64.2
63.0
63.5
64.2
63.0
63.5
64.2
UCCH
2023
60.4
61.4
61.8
60.4
61.4
61.8
60.4
61.4
61.8
60.4
61.4
61.8
COCO
CMMQ
2022
43.9
44.1
44.6
39.8
39.8
40.2
35.9
35.5
35.5
33.7
33.7
33.7
MIAN
2023
61.0
63.8
64.8
60.8
62.9
63.6
59.2
60.2
60.5
57.3
59.4
58.7
LICMH
2023
58.1
60.5
62.1
56.3
58.9
60.4
54.7
56.3
57.9
52.3
54,2
55.3
DHRL.
2024
33.4
34,5
61.9
33.3
43.6
60.4
33.2
43.2
61.5
35,2
60.6
59.8
NRCH
2024
65.9
67.3
67.8
65.6
66.9
67.3
64.9
66.8
67.1
63.2
65.3
66.2
RSHNL
2025
60.7
60.3
60.2
61.1
61.5
61.7
59.9
59.9
61.7
60.7
59.1
60.3
NACD
Ours
66.7
68.4
68.7
67.2
68.4
69.1
67.1
68.5
69.0
66.5
68.3
68.8
Table 2: The ablation study results on NUS and COCO datasets with 128 bits and across various redundant rates. The highest scores are presented in bold.
NUS
COCO
Method
1.0
1.5
2.5
1.0
1.5
2.5
only Lnaer
34.3
33.5
33.3
36.0
33.3
33.3
33.3
33.3
only Lerch
63.3
63.0
63.1
63.2
64.9
64.9
65.6
65.7
NACD w/o CM strategy
69.2
69.1
69.2
69.0
67.2
66.9
67.1
67.1
NACD la remains 0.5
70.0
69.5
69.3
68.9
67.9
68.3
68.3
68.0
NACD w/o Lquent
69.0
69.0
68.4
68.1
68.0
67.9
67.9
67.8
NACD w/o warm-up
69.3
69.2
68.4
68.0
68.1
The full NACD
70.3
70.2
70.2
69.6
68.7
69.1
69.0
68.8
As shown in Fig. a(a:) (1) Both NACD-1 and NACD-2 tend to overfit redundant noise, indicating that the CM strategy and class-wise threshold updating are crucial for enhancing the robustness of NACD. (2) Although NRCH exhibits certain robustness under redundant annotations, it still lags

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significantly behind NACD. This gap widens as noise levels increase, further underscoring NACD's robustness in challenging scenarios.
Disambiguation Analysis. In Fig. cJ)) we analyze the model's s ability to disambiguate redundant annotations by computing the average pseudo-label length for all sample pairs in the COCO dataset. Here, the pseudo-label length for the j-th sample pair is defined as the number of positive entries in Пz reflecting how many classes the model predicts positive. As shown the figure, when the redundant rate decreases, the average pseudo-label length gradually approaches the average number of ground-trute labels in the COCO dataset (2.76). This indicates that NACD can accurately recover the true label subset from redundant annotations, confirming its strong disambiguation capability.
0.70
0.70
Redundem: Rete:1.
0.65
Average Length
Redundani Rate:1.5
Redundant Rate
Redundien Rate2.5
MAP
0.60
MAP
0.60
0.55
0.55
4.
0.50
NACD-1
0.50
NACO-1
NACD-2
NACD-2
3
MIHH
MICH
0.45
0.45
20
40
60
80
100
20
40
60
80
100
20
40
80
100
Epoch
Epoch
Epoch
(a) redundant rate of 2.0.
(h) redundant rate of 2.5.
(c) average label length.
Figure 5: The robustness study and disambiguation study results with 32 bits on the COCO dataset.
Conclusion
In this paper, we propose novel Neighbor-awee aware Contrastive Disambiguation (NACD) framework to address the challenge of redundant annotations in cross cross-modal hashing. NACD comprises two key modules: Neighbor-aware Confidence Reconstruction (NACR) and Class-aware Robust Contrastive Hashing (CRCH). Specifically, NACR reconstructs label confidence by aggregating information from cross-modal neighbors, therehy distinguishing true labels from ambiguous ones. Meanwhile, CRCH constructs reliable positive and negative pairs based label confidence, substantially enhaneing robustness under noisy supervision. Moreover, a quantization loss is incorporated to reduce the quantization error and enforce binary constraints on the learned hash representations. Extensive experiments on three large-scale multimodal benchmarks demonstrate that NACD consistently outperforms state-of suuee-ofthee-ae approaches, showing strong robustness and stable performance for coosl hashing with redundant annotations.
Limitation. Although limitations that need to be addressed. In this paper, the "redundant annotations" we study in cross- modal hashing do not account for the inherent similarity between labels. NACD may struggle when noisy labels are highly similar to the ground-truth, such as when the correct label is "car" and the noisy label is "truck". Additionally, we only conduct extensive experiments on image and text modalities to demonstrate NACD's effectiveness. In the future, additional modalities need to be considered to verify the generalization ability of NACD. We encourage further research to better understand and mitigate the limitations and risks of cross-modal hashing with redundant annotations.
proposed NACI) demonstrates strong performance, there are still some
Acknowledgments
This work was supported by the National Natural Science Foundation of China (62306197, 62372315), Sichuan Science and Technology Planning Project (20252NSFSC15077, 2024YFG0007, 2024ZDDZX0004, 2024NSFTD0049), China Postdoctoral Science Foundation (202ITQ0223, 2022M712236), Chengdu Science and Technology Project (2023-XT00000004-GG), Postdoctoral
Joint Training Program of Sichuan University (SCDXLHPY2307),
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Answer: [Yes]
This paper proposes a novel Neighbor-awars Contrastive Disambiguation (NACD) method for cross-modal hashing with redundant annotations. 'hee effectiveness this method thoroughly demonstrated through extensive experiments, which precisely align with the main claims made at the beginning.
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[NA]
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This is not theoretical paper.
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17

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Appendix
In the following sections, we provide additional details about the optimization process (Sec. A). the datasets (Sec. B). and the experimental results (Sec. C of proposed NACD.
A Optimization
To effectively learn under redundant amnotation supervision, we design an end-to-end optimization strategy, summarized in Algorithm [ During the warm-up phase, train the model without the CM strategy and the class-wise threshold update. Once the warm-up phase is complete, the stabilized model produces more reliable feature representations, which in turn provide stronger support for updating label confidences through neighborhoodd-based mechanisms. In addition, before backpropagation, we slightly update the label confidences of the current mini-batch based on the model's latest predictions to refine the supervision signals.
Algorithm 1
Optimization Algorithm for NACD
Require: The redundant annotations training set D. the code length L, the network N {fi(;, Q1), f2(-, 02)). the learnable matrix W1, W2, the maximal epoch number Tmaxs and the warm-up epoch number T Tarmm
1: Randomly initialize network parameters (O,, W
2: Use part of the multi-partin labels of each sample as the initial state of instance confidence, and set the threshold for each class 0.5.
3: for epoch 1 to Tmax do
4: for D, in mini-batches sampled from D do
5: Compute fintt by Eq. I
6: Update instance confidence by Eq. T
7: Optimize network parameters through backpropagation; end for if epoch Twarm then
8:
9:
10:
Select the neighbor update instance label confidence by Eq. 2 Update class-aware threshold by Eq. (5);
11:
12: end
end for
Ensure: Network parameters {O,, W.
B Datasets
To verify the effectiveness of our proposed NACD method in addressing the redundant annotations problem, we conduct experiments on the MIRFlickr-25k (Flickr) 42). NUS- WIDE (NUS) [43]. and MS-COCO (COCO) 44 datasets. The details are as follows: MIRFlickr-25K 25K image-tex text pairs, each belonging to one of 24 categories. In this work, we only select 20,015 pairs multimodal dataset containing 81 concept categories.
contains 25 500
that have annotations. NUS- WIDE 43 In this work, we only consider the subset of data from the most frequent 21 categories, which includes 190,421 image-text pairs. MS-COCO 44 encompasses vast collection of 123,287 images distributed across 80 diverse categories. Each image is enriched with five detailed textual descriptions. After considering only labeled data, we ultimately select 122,2 18 image-text pairs.
Following dataset partition strategies adopted in prior works [22 [0), we have structured the datasets accordingly: For MIKF 42], we select 2,000 data points the test (query) dataset. The remaining data points are used to form the retrieval (database) dataset. From this, we further identified training subset comprising 10,000 data points. the case of NUS-W dataset is made up of 2, 100 data points. The remaining data points form the retrieval (database) dataset. From this dataset, we select 10,500 data points for training. For MS-COCO (44, we extract 5,000 data points to be used for testing. The rest of the data points are pooled into the retrieval (database) dataset. From this, we set aside 10,000 data points specifically for training. 3 the specific data split information of these three multimodal datasets in experiments.
43). the test (query)
21

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Table 3: Data split and basic information for Flickr, NUS, and COCO in our experiments.
Dataset
Test (query)
Database
Train
Average length of GTss
Classes
Flickr NUS
2,000
18,015
10,000
3.78
24
2,100
188,321
10,500
2.09
21
COCO
5,000
117,218
10,000
2.76
80
Experimental Results
C.1
Additional Comparative Experiments
Moreover, to further validate the effectiveness of method in a more comprehensive manner, conduct additional experiments using 8-bit and 16-bit hash codes. The results of these experiments are presented in Table demonstrating the stable and competitive performance of our approach under various experimental settings.
C.2 Additional Parameter Analysis
To analyze the impact of the coefficients X, and [ in Eq. and Eq. (11), conduct parameter analysis experiments on the Flickr and NUS datasets under different redundant rates. The results illustrated in Fig. 6 Specifically, for the Flickr dataset, the model achieves its peak performance when À is within [0.1, 0.3], a within 10 0.5), and /3 within [0.5, 1]. This indicates that relatively balanced tuning of these parameters is crucial to optimize the model's performance
this dataset. Similarly, for the NUS dataset, the model exhibits optimal performance when À is within [0.05, 0.5), within [0.3, and 3 within [0.5, I]. These findings highlight the necessity of dataset-specific parameter tuning to maximize model performance.
0.80
0.78
0.75
0.76
0.76
0.70
MAP
MAP
0.75
0.75
0.65
0.74
Redundant Rate:1.0
Redundant Rate:1.0
0.74
Redundant Rate:1.0
Redundant Rate:1.5
Redundant Rate:1.5
0.73
Redundant Redundant Rate:2.5
0.60
Redundant Redundant Rate:2.5
0.73
Redundant Rate:2.0
Redundant 2.5
0.72
0.01 0.05 0.1 0.2 0.3 0.6 0.8
0.55
0.03 0.05 0.1 0.3 0.5
0.72
0.1
0.5 0.7
10
(a) À on Flickr.
(b) a on Flickr.
(c) / on Flickr.
0.71
0.70
0.71
0.65
0.70
0.70
0.60
0.69
MAP
0.69
MAP
0.55
0.68
0.50
0.67
Redundant Rate:1.0
0.45
Redundant Rate:1.5
Redundant Rate:1.5
Redundant Rate:1.5
0.67
Redundant Rate:2.0
0.40
Redundant Rate:2.0
0.66
Redundant Rate:2.0
Redundant Rate:2.5
Redundant Rate:2.5
Redundant Rate:2.5
0.66
0.65
0.1
0.5
0.7
2
a
B
(c) NUS.
(f) NUS.
(g) NUS.
Figure 6: The performance of NACD terms of average MAP scores versus different values of X, and the Flickr and NUS datasets using 128 bits.
22

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Table 4: The performance comparison in terms of average MAP scores (%) of 12T and T2I tasks under different redundant rates and various bit lengths the MIRFFickr-25K(Flicte) NUS-WIDE(NUS). and COCOCCOCOOO datasets. The highest and second highest MAP scores among all methods are shown bold and underline respectively.
1.0
1.5
2.0
2.5
Dataset
Method
Year
8bits
16bits
8bits
16bits
8bits
16bits
8bits
16bits
DISRH
2019
61.3
61.4
61.3
61.4
61.3
61.4
61.3
61.4
DGCPN
2021
69.0
67.9
69.0
67.9
69.0
67.9
69.0
67.9
PIP
2021
66.9
67.4
66.9
67.4
66.9
67.4
66.9
67.4
CIRH
2022
65.6
66.8
65.6
66.8
65.6
66.8
65.6
66.8
UCCH
2023
67.5
70.1
67.5
70.1
67.5
70.1
67.5
70.1
Flickr
CMMQ
2022
58,1
60.5
61.9
62.7
56.5
61.8
56.5
61.8
MIAN
2023
65.5
68.6
64.1
59.7
61.7
58.9
59.9
LICMH
2023
60.7
61.4
58.7
57.8
55.6
54.9
56.2
55.0
DHRL
2024
67.1
68.7
65.7
68.2
65.3
67.6
65.3
66.5
NRCH
2024
67.0
71.0
53.9
58.5
53.9
57.9
53.9
56.9
RSHNL
2025
70.3
71.5
68.8
71.0
68.7
70.2
68.9
70.9
NACD
Ours
73.9
75.9
73.6
75.0
72.5
74.8
70.2
73.3
DISRH
2019
42.8
42.9
42.9
42.8
42.8
42.9
DGCPN
2021
55.2
58.6
55,2
58.6
$5.2
58.6
55.2
58.6
2021
55.7
56.2
55.7
56.2
55.7
55.7
56.2
CIRH
2022
54.6
55.5
54.6
55.5
54.6
55.5
54.6
55.5
UCCH
2023
60.1
57.4
60.1
57.4
57.4
60.1
NUS
CMMQ
2022
56.4
57.4
52.1
52.3
46.2
45.5
38.5
37.6
MIAN
2023
58,9
62.1
54.8
55.2
$4.2
54.4
50.0
54.2
LICMH
2023
52.2
53.0
36.9
46.1
38.6
45.3
38.0
44.1
DHRL
2024
58.3
59.3
54.0
58.9
54.0
57.3
52.6
56.8
NRCH
2024
63.7
65.9
61.8
65.4
61.1
64.9
59.2
64.6
RSHNL
2025
61.1
57.6
54.5
50.9
55.0
NACD
Ours
62.9
65.0
61.33
66.1
61.3
65.8
62.1
65.2
DJSRH
2019
41.9
47.6
41.9
47.6
41.9
47.6
41.9
47.6
DGCPN
2021
56.6
61.1
56.6
61.1
56.6
61.1
56.6
61.1
PIP
2021
46.2
52.6
46.2
52.6
46.2
$2.6
46.2
52.6
CIRH
2022
54.2
59.6
54.2
59.6
54.2
59.6
54.2
59.6
UCCH
2023
55.3
57.0
55.3
57.0
55.3
$7.0
55.3
57.0
COCO
CMMQ
2022
45.4
44.2
39.8
36.1
33.8
33.7
MIAN
2023
53.9
58.5
55.0
58.4
53.0
57.4
52.4
56.2
LICMH
2023
52,3
54.7
51.2
51.2
50.0
$1.5
40.0
49.6
DHRL
2024
34.4
33.4
33.4
33.8
33.4
33.8
33.4
42.1
NRCH
2024
59.5
64.2
58.7
63.3
57.5
62.0
56.2
61.0
RSHNL
2025
56.6
59.3
55.4
59.7
$7.6
61.3
55.2
59.3
NACD
Ours
62.1
65.5
61.7
65.4
62.3
65.6
60.5
65.8
23


Reviews:
[{"review_id": "bFrAOSUVIK", "reviewer": "Reviewer_agwg", "summary": "This paper innovatively investigate the redundant annotation problem in cross-modal hashing, which is of great research significance. The paper proposes a method termed Neighbor-aware Contrastive Disambiguation (NACD) to address this novel problem, which ensures robust learning under redundant supervision. Both the Neighbor-aware Confidence Reconstruction (NACR) and the Class-aware Robust Contrastive Hashing (CRCH) modules in NACD are highly innovative and achieve retrieval performance that far exceeds state-of-the-art methods. The comprehensive experiments well demonstrate the authors' viewpoints and motivations, endowing the paper with high research value.", "questions": "Please see the weakness and response each comment. Besides, Additionally, the authors should also answer the following question: \nNACR heavily relies on the label information of neighbor sample pairs. Under annotation conditions with a high redundant rate, how can the performance of NACR be guaranteed?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1)This paper is the first to study the redundant annotation problem in cross-modal hashing.\n\n2)The proposed NACD achieves SOTA performance, especially demonstrating robust learning under redundant supervision.\n\n3)The experiments in this paper are not only abundant but also well demonstrate the authors' viewpoints and motivations, endowing the paper with high research value.\n\n4)The writing is straightforward, clear, and easy to understand.\n\n\nWeaknesses:\n\n1)The label connections between the anchor and neighbor sample pairs in Figure 1 are not well explained. These connections should be elaborated when introducing the NACR module based on Figure 1.\n\n2)Further explanation is needed on why the dynamic class threshold can achieve robust contrastive learning. Compared with previous methods that use fixed thresholds, what are the advantages of the class-wise threshold updating strategy in CRCH?\n\n3)In the section explaining the experimental results, there is no explanation as to why noise label learning methods such as DHRL, NRCH, and RSHNL are not able to effectively deal with Redundant Annotations.", "was_revised": true, "final_justification": "The authors’ response has addressed my comments well. I will maintain my original score.", "rebuttal": "Thank you for your valuable comment. Below are the responses to your comments.\n\n**Q1.** The label connections between the anchor and neighbor sample pairs in Figure 1.\n\n**A1.** Thanks for your comment. In this paper, we investigate the task of cross-modal hashing between image and text modalities. Each image-text pair is treated as an anchor, and the similarity between the anchor and other sample pairs is computed as the average cosine similarity of their respective image and text components. Motivated by the assumption that **semantically similar samples tend to share common labels**, the top 20 most similar neighbors are selected based on this measure, and their label confidence is incorporated into the NACR module.\n\nFigure 1 illustrates an example of an anchor and one of its neighbors. The anchor has a redundant annotation set of **{Sea, Car, Mountain, Beach, Plant, Cloud, Dog, Human}**, among which **{Sea, Beach, Plant, Cloud}** are the true labels. The neighbor has a redundant annotation set of **{Sea, Horse, Airplane, Boat, Plant, Bridge, Building, Human}**, with **{Sea, Boat, Plant, Building}** being the true labels. While each sample pair possesses its own ground-truth label set, they exhibit partial overlap in true labels as **{Sea, Plant}**— a property that reflects the underlying semantic structure in feature space and motivates the design of the NACR mechanism.\n\nWe will further clarify the label connections between the anchor and its neighbors in Figure 1 and elaborate on their role when introducing the NACR module in the revised version of the paper.\n\n**Q2.** Further explanation is needed on why the dynamic class threshold can achieve robust contrastive learning. Compared with previous methods that use fixed thresholds, what are the advantages of the class-wise threshold updating strategy in CRCH?\n\n**A2.** Thanks for your valuable question. Contrastive learning methods heavily rely on high-quality labels to construct effective positive and negative sample pairs. However, due to differences in class difficulty, occurrence frequency, and semantic ambiguity, the confidence distributions of pseudo-labels often vary significantly across categories. If the threshold is too loose for high-confidence classes, it may introduce noisy pseudo-labels; on the other hand, if it is too strict, it may incorrectly filter out truly relevant labels. Therefore, applying a dynamic threshold for each class is crucial for obtaining high-quality pseudo-labels. The class-wise dynamic threshold mechanism addresses this issue by aligning with the unique uncertainty characteristics of each category. It enables the retention of confident pseudo-labels while effectively filtering out noisy ones.\n\nIn CRCH, class-wise thresholds are computed based on the statistical properties of reliable intra-class samples. These statistics are inherently robust to noise, allowing the model to suppress the influence of low-quality labels. More importantly, the thresholds are adaptively updated in response to the evolving confidence distribution. As the model improves and reconstructs more accurate label confidences, the dynamic thresholding mechanism evolves accordingly. This progressive refinement avoids the cumulative bias commonly observed in static thresholding methods, ensuring a continual improvement in pseudo-label quality throughout training. The results of the ablation study (Table 2) also well demonstrate the effectiveness of the class-wise threshold updating strategy.\n\nIn the final version of the paper, we will further elaborate on the principles and advantages of the class-wise threshold updating strategy in the Methods section.\n\n**Q3.** In the section explaining the experimental results, there is no explanation as to why noise label learning methods such as DHRL, NRCH, and RSHNL are not able to effectively deal with Redundant Annotations.\n\n**A3.** Thank you for the insightful question. Methods like DHRL, NRCH, and RSHNL are designed for noisy label scenarios where only a subset of training samples are affected by label noise. These approaches typically aim to **identify and correct mislabeled samples** while assuming that some of the data remains clean. However, in the redundant annotations setting, every sample is associated with a candidate label set that includes both true and redundant labels. This means **the label information for every sample pair is ambiguous**, and the challenge becomes one of label disambiguation, not just noise filtering. Since these methods lack explicit mechanisms to separate true labels from redundant ones at the per-sample level, they are less effective under this more difficult setting, as demonstrated in our experimental results.\n\nIn contrast, our method explicitly tackles this challenge through two key components: Neighbor-aware Confidence Reconstruction (NACR) and Class-aware Robust Contrastive Hashing (CRCH). NACR aggregates label confidence from semantically similar cross-modal neighbors to refine the confidence scores for each candidate label, enabling more accurate identification of true labels. Meanwhile, CRCH constructs contrastive pairs based on dynamically updated class-wise confidence thresholds, which helps reduce erroneous associations caused by noisy labels. Together, these modules allow our framework to effectively disambiguate labels and maintain robust performance, even under high levels of redundant supervision.\n\nIn the final version of the paper, we will provide a more detailed explanation in the experiments section regarding the reasons for the poor retrieval performance of noisy label learning methods such as DHRL, NRCH, and RSHNL.\n\n**Q4.** NACR heavily relies on the label information of neighbor sample pairs. Under annotation conditions with a high redundant rate, how can the performance of NACR be guaranteed?\n\n**A4.** Thanks for your valuable comment. While NACR relies on the label information of neighbor sample pairs, it is robust under high redundant annotation rates. This is achieved through two core strategies: (1) cross-modal neighbor aggregation, which combines label confidence from both image and text modalities to reduce modality-specific noise; and (2) similarity-weighted fusion, which ensures that only semantically relevant neighbors have strong influence. These mechanisms help filter out noisy signals and reinforce true semantic patterns. As demonstrated in our main experiments, ablation studies, and robustness analysis (e.g., Table 1 , Table 2, and Fig. 4), NACR consistently maintains high performance even when the redundant rate reaches 2.5, demonstrating its robustness against noisy supervision."}, {"review_id": "QAPdhtbFBp", "reviewer": "Reviewer_pMFp", "summary": "Unlike previous weakly supervised learning for cross-modal hashing, this paper, starting from the perspective of practical annotation, innovatively proposes a new weakly supervised learning paradigm called Redundant Annotations (RA). To achieve robust retrieval performance under redundant supervision, this paper specifically proposes the Neighbor-aware Contrastive Disambiguation (NACD) method, which includes the NACR and CRCH modules. Experiments have proven the effectiveness of both NACR and CRCH. Overall, this paper sets a new standard for the new problem of Cross-Modal Hashing with Redundant Annotations and is a solid and noteworthy piece of excellent work.", "questions": "Please refer to the weaknesses.", "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": "S1. The redundant annotation setting reveals a new weakly supervised learning paradigm in the cross-modal hashing task.\nS2. The NACD method designed in the paper effectively addresses the problem of Redundant Annotations and achieves excellent retrieval performance.\nS3. The experiments illustrate the effectiveness and efficiency of the proposed method.\n\nW1. In section 3.3, further explanation is needed as to why the Intersection over Union (IoU) between pseudo-labels is used to compute the label similarity matrix.\nW2. It needs to be explained why the CRCH module can achieve robust contrastive learning.\nW3. Figure 4 (c) is not detailedly introduced.", "was_revised": true, "final_justification": "Thank you for the author's response. My problems have been properly resolved. I have decided to keep my score.", "rebuttal": "Thank you for your valuable comment. Below are the responses to your comments.\n\n**Q1.** In section 3.3, further explanation is needed as to why the Intersection over Union (IoU) between pseudo-labels is used to compute the label similarity matrix. \n\n**A1.** Thanks for your valuable comment. When constructing contrastive sample pairs, we treat pairs with no overlapping pseudo-labels as negative pairs. The remaining pairs, which share partially or fully overlapping labels, are considered positive pairs. Since these positive pairs exhibit **varying degrees of semantic similarity**, a soft metric is required to effectively capture their nuanced differences.\n\nTo this end, we adopt the Intersection over Union (IoU) to measure label similarity. IoU focuses on the overall structure of the label sets and is inherently robust to the sparsity of multi-label annotations. Unlike simple vector-based similarity metrics, IoU is not affected by the absolute number of labels, thereby **eliminating bias caused by varying label lengths**. Moreover, it is less sensitive to the absence or inclusion of individual labels, leading to more stable and consistent similarity estimations across samples.\n\n**Q2.** It needs to be explained why the CRCH module can achieve robust contrastive learning.\n\n**A2.** Thank you for your valuable suggestions. To enable robust contrastive learning, CRCH relies on high-quality labels to construct meaningful positive and negative sample pairs. However, applying a fixed threshold across all categories fails to account for the variability in confidence distributions, which arise from differences in class difficulty, occurrence frequency, and semantic ambiguity.\n\nTo address this, CRCH introduces a class-wise dynamic thresholding mechanism, which adaptively adjusts the threshold for each category based on the statistical properties of its reliable samples. This allows the model to obtain more reliable pseudo-labels while effectively filtering out noisy ones in a class-specific manner. As training progresses and confidence reconstruction becomes more accurate, the thresholds are dynamically updated to reflect the evolving label distributions.\n\nIn the contrastive learning process, we adopt a label-similarity-guided loss function for positive pairs, which fully leverages the improved label quality resulting from class-wise thresholding. For negative pairs, we further **distinguish between hard and easy negatives**, enabling more targeted optimization and enhancing the discriminative power of the learned hash codes. We will provide a detailed explanation in the final version of the paper regarding why the CRCH module can achieve robust contrastive learning.\n\n**Q3.** Figure 4 (c) is not detailedly introduced.\n\n**A3.** Thank you for the insightful question. As shown in Figure 4, we present the variation in the average number of labels per sample on the MS-COCO dataset during training. The ground-truth average label set length in MS-COCO is 2.76. Under four different redundant ratios of 1.0, 1.5, 2.0, and 2.5, the initial average label set lengths of the noisy training set are 5.52, 6.9, 8.28, and 9.66, respectively.\n\nDuring the warm-up phase, since both the label confidence reconstruction and class-wise threshold updating are disabled, the average label set length remains unchanged. After the warm-up phase, a category is regarded as a pseudo-label only when its confidence exceeds the corresponding class-wise threshold. Under the joint effect of confidence reconstruction and adaptive thresholding, the average pseudo-label set length progressively approaches the true label set length.\n\nMoreover, we observe that the lower the redundancy ratio, the closer the final label set length is to the ground-truth average label set length. Even under higher redundancy settings, the number of retained labels per sample eventually becomes significantly smaller than the initial noisy label count. This demonstrates the effectiveness of our method in suppressing label noise and recovering clean supervision signals. We will improve the explanation of Figure 4(c) in the revised version of the paper."}, {"review_id": "0A0xN7K2Oj", "reviewer": "Reviewer_2t6S", "summary": "This paper proposes a new task called Cross-Modal Hashing with Redundant Annotations, which holds significant research value. The authors introduce the Neighbor-aware Contrastive Disambiguation (NACD) method to tackle the challenge of this new task: robust learning under redundant supervision. NACD incorporates two key components. The first, Neighbor-aware Confidence Reconstruction (NACR), refines label confidence by aggregating information from cross-modal neighbors to distinguish true labels from redundant noisy ones. The second, Class-aware Robust Contrastive Hashing (CRCH), constructs reliable positive and negative pairs based on label confidence scores, which significantly enhances robustness against noisy supervision. In addition to these components, this work also designs a warm-up strategy and a quantization loss to further boost retrieval performance. The paper is well-structured and easy to understand, effectively elucidating the research motivations. It is believed that this paper will bring new research ideas to the traditional task of cross-modal hashing.", "questions": "a) The principle of the quantization loss should be claried.\n\nb) Please evaluate and explain the robustness of CRCH.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\na) Redundant Annotations is a labeling issue that is worth studying and has practical significance.\n\nb) The empirical results show that NACD does help increase the performance for a dataset with Redundant Annotations.\n\nc) The paper is highly persuasive in elaborating the motivations and highly innovative in NACD.\n\nWeaknesses:\n\na) In the Introduction section, it is not quite clear what the differences and advantages are between Redundant Annotations and noise label annotations.\n\nb) In section 4.5, how to understand the robustness demonstrated in Figure 4?\n\nc) The font size in Figure 2 should be appropriately increased.", "was_revised": true, "final_justification": "Thank you for the authors' responses. In the rebuttal, the autohrs explained the motivation and principle of the quantization loss, and robustness against redundant annotations. I raise my score to Accept.", "rebuttal": "Thank you for your valuable comment. Below are the responses to your comments.\n\n**Q1.** The differences and advantages are between redundant annotations and noisy label annotations.\n\n**A1.** Thank you for your comment. To the best of our knowledge, redundant annotations represent a novel paradigm in the field of cross-modal hashing that has not yet been explored. It differs from the conventional noisy label learning paradigm in the following aspects:\n\n(1) **In terms of label construction:** redundant annotations retain all original ground-truth labels and augment them with additional noisy labels to form a candidate label set. In contrast, noisy labels typically involve replacing some correct labels with incorrect ones.\n\n(2) **In terms of motivation:** The redundant annotations setting is inspired by the real-world annotation practice of **\"trying not to miss any ground truth\"** — annotators tend to include more potentially relevant labels, especially for uncertain or difficult samples, to avoid missing true labels. On the other hand, the noisy labels setting simulates annotation errors or misunderstandings, where label noise arises from subjective mistakes or system-level inaccuracies.\n\n(3) **In terms of learning challenges:** redundant annotations primarily focus on disambiguating the ground-truth labels from a larger candidate label set. In contrast, noisy label learning emphasizes identifying and handling incorrectly labeled samples.\nTherefore, redundant annotations better reflect practical annotation scenarios, where annotators prefer to include more candidate labels to ensure that the ground truth are not omitted — especially for ambiguous or challenging instances. \n\nMoreover, redundant annotations can retain as much correct label information as possible, which enables the model to fully leverage semantic supervision during training. This facilitates better modeling of cross-modal semantic correlations, thereby enhancing the discriminative power of the hash codes and improving retrieval performance. In the final version of the paper, we will provide a detailed explanation of the differences and advantages between redundant annotations and noisy label annotations in the introduction section.\n\n**Q2.** Explain the robustness demonstrated in Figure 4.\n\n**A2.** Thank you for the insightful question. Figure 4(a)(b) without the class-wise threshold mechanism, the NACD variant attains its highest MAP around 40 epochs before declining, indicating overfitting during training. Conversely, the complete NACD model either improves or maintains stable performance over 100 epochs, ultimately delivering superior results. Moreover, Figure 4(c) intuitively demonstrates that NACD can effectively extract the true labels from redundant annotations progressively through training. We will provide a detailed explanation in the final version of the robustness demonstrated in Figure 4.\n\n**Q3.** The font size in Figure 2 should be appropriately increased.\n\n**A3.** Thanks for your valuable suggestion. We will increase the font size in the figures and further enhance their appearance in the next version of the paper.\n\n**Q4.** The principle of the quantization loss.\n\n**Q4.** Thank you for your valuable comment. In cross-modal hashing, compact binary hash codes are employed to compute Hamming distances for efficient retrieval. However, during the training phase, the sign function—which is required for strict binarization—is non-differentiable and thus **unsuitable for gradient-based optimization**. To address this, continuous approximations such as the hyperbolic tangent (tanh) function are commonly adopted.\n\nAs a result, the model generates real-valued features in the range of [-1, 1], rather than ideal binary codes. When these continuous features are eventually binarized using the sign function during inference, additional quantization error is introduced—leading to information loss that is not accounted for during training.\n\nTo bridge this gap, a quantization loss is introduced as a regularization term. This loss encourages the real-valued outputs to remain close to their corresponding binary codes throughout training, thereby reducing the discrepancy between the learned features and the final hash codes. By aligning the optimization objective with the discrete nature of Hamming retrieval, quantization loss effectively **mitigates post-training information degradation** and improves overall retrieval performance.\n\n**Q5.** Evaluate and explain the robustness of CRCH.\n\n**A5.** Thank you for your insightful questions. CRCH leverages the reconstructed label confidences to establish a threshold for each category, enabling the generation of more reliable pseudo-labels. This facilitates the construction of more accurate positive and negative pairs for contrastive learning. \n\nIn terms of contrastive learning, we adopt a label-similarity guided loss function for positive pairs, which effectively utilizes the improved label quality brought by class-wise thresholds. For negative pairs, we **further distinguish between hard negatives and easy negatives**, allowing for more targeted and effective learning.\n\nAs illustrated in Figure 4(a)(b) of the paper, when the class-wise threshold mechanism is removed, the NACD variant reaches its peak MAP at around 40 epochs and then begins to decline, indicating overfitting during training. In contrast, the full version of NACD continues to improve or **remains stable throughout 100 epochs**, ultimately achieving better performance. We will provide more elaboration on the robustness of CRCH in the final version."}, {"review_id": "w1R1iVVd8p", "reviewer": "Reviewer_ETEA", "summary": "The paper proposes a novel Neighbor-aware Contrastive Disambiguation (NACD) approach, which is the first work to focus on the scenario of redundant annotations in the cross-modal hashing task. This work primarily involves two key innovative modules: a Neighbor-aware Confidence Reconstruction (NACR) which refines label confidence by aggregating information from cross-modal neighbors to achieve label disambiguation, and a Class-aware Robust Contrastive Hashing (CRCH) module for robustly reducing modality gaps under redundant supervision. Additionally, this work further enhances model performance by introducing an innovative quantization loss that enforces binary constraints on the learned hash representations. Importantly, the experiments in this paper are extremely comprehensive and meticulous, not only demonstrating the advanced performance of NACD and the effectiveness of each module, but also providing additional evidence for the robustness of NACD and the effectiveness of label disambiguation through more experiments.", "questions": "- Referring to Eq (2), how should the number of neighbors and the computation be balanced?\n\n- The Redundant Annotations in this paper assume all correct labels are annotated. How to consider scenarios where some true labels are missed?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The problem explored in this paper---cross-modal hashing with redundant annotations---is very practical and interesting.\n\n- The proposed method is generally very intuitive and I think it will be effective.\n\n- The experiments in this paper are extremely comprehensive and meticulous.\n\nWeaknesses:\n\n- Why are the redundant rates in the paper set to 1.0, 1.5, 2.0, and 2.5? Are excessively high redundant rates not realistic in actual annotation scenarios?\n\n- The role of the warm-up strategy needs to be clarified.", "was_revised": true, "final_justification": "The authors have addressed all my concerns, and taking into account the feedback from other reviewers, I recommend that this work be accepted.", "rebuttal": "Thank you for your valuable comment. Below are the responses to your comments.\n\n**Q1.** Why are the redundant rates in the paper set to 1.0, 1.5, 2.0, and 2.5? Are excessively high redundant rates not realistic in actual annotation scenarios?\n\n**A1.** Thank you for the insightful question. The redundant annotations setting is inspired by the real-world annotation practice of **\"trying not to miss any ground truth\"** — annotators tend to include more potentially relevant labels, especially for uncertain or difficult samples, to avoid missing true labels. Thus the redundant rates of 1.0, 1.5, 2.0, and 2.5 in our paper are chosen based on both practical annotation behavior and alignment with prior works.\n\n(1) we agree that excessively high redundant rates are not realistic in practical annotation, as **human annotators typically do not assign an overwhelming number of labels per instance**. To reflect this, our selected range (1.0–2.5) intentionally avoids extreme noise and instead focuses on realistic and representative levels of annotation redundancy.\n\n(2) our choice is **consistent with previous work[1]**, which adopt the same or similar redundant rate settings to evaluate robustness in partial multi-label learning frameworks. This consistency helps ensure fair comparison and benchmarking.\n\nIn summary, the selected redundant rates provide a balanced coverage from mild to relatively challenging noise conditions, reflecting real-world tendencies while maintaining comparability with prior work.\n\n**References**\n\n[1] Hang J Y, Zhang M L. Partial multi-label learning with probabilistic graphical disambiguation[J]. Advances in Neural Information Processing Systems, 2023, 36: 1339-1351.\n\n**Q2.** The role of the warm-up strategy needs to be clarified.\n\n**A2.** Thank you for your insightful comment regarding the warm-up strategy. Our neighbor-aware confidence reconstruction relies on cross-modal neighborhood features. However, **during the warm-up phase, the model is not yet sufficiently trained**, and the output features—both across modalities and within the same modality—are still unstable. This instability can lead to inaccurate neighborhood identification, which in turn compromises the reliability of the reconstructed label confidence.\n\nTo mitigate this issue, we **disable the neighbor-aware confidence reconstruction module during the warm-up stage and retain only the contrastive learning objective**. This allows the model to focus on reducing cross-modal discrepancies and stabilizing feature representations. Once the features become more reliable, we reintroduce the confidence reconstruction to provide a more accurate estimation of sample-level label confidence.\n\nAs shown in the algorithm's pseudocode, we resume both the instance label confidence update and the computation of class-wise thresholds after the warm-up phase. We will improve the introduction of the role of the warm-up strategy in the next version of the paper.\n\n**Q3.** Referring to Eq (2), how should the number of neighbors and the computation be balanced?\n\n**A3.** Thank you for the insightful question. It is true that a small number of neighbors may lead to unreliable label estimation, while a large number of neighbors increases noise and computational cost. As a result, I followed previous conventions and set the number of neighbors to 20, which provides sufficient semantic context from relevant samples while maintaining tractable overhead. In the revised version of the paper, we will include a more detailed explanation regarding the choice of the number of neighbors.\n\n**Q4.** How to consider scenarios where some true labels are missed?\n\n**A4.** Thank you for the valuable question. Our current setting follows the standard Partial Multi-Label Learning (PML) framework, where the candidate label set (also referred to as redundant annotations) includes all ground-truth labels, along with a certain number of incorrect labels. This assumption reflects the common annotation practice of **\"trying not to miss any ground truth\"**, where annotators tend to include potentially relevant labels to avoid missing any true ones.\n\nHowever, we acknowledge that in real-world applications, missing true labels is also possible, leading to more challenging scenarios. Addressing such cases would require more advanced strategies. As illustrated in Figure 4(a)(b), the proposed NACD model can achieve robust retrieval performance under the interference of redundant noise labels. Moreover, Figure 4(c) intuitively demonstrates that NACD can effectively **extract the true labels from redundant annotations** progressively through training.\n\nWe consider this direction highly meaningful and complementary to our current work. In future extensions, we plan to explore the scenarios where some true labels are missed, to better align with practical annotation noise."}]

Author rebuttal:

Meta-review: The paper introduces Neighbor-aware Contrastive Disambiguation, a novel method for cross-modal hashing under redundant annotations. Key contributions: 1） It is a novel work to address redundant annotations in cross-modal hashing, a practical yet underexplored problem. 2）It combines Neighbor-aware Confidence Reconstruction for label disambiguation and Class-aware Robust Contrastive Hashing for robust retrieval. The experimental results show that the method outperforms baselines significantly, especially under high redundant supervision. Overall, the method is theoretically sound, empirically validated, and well-motivated. All reviewers recommend acceptance to the paper after the rebuttal. Therefore, the final decision is accepted.
Decision: Accept (spotlight)