Deep Global-sense Hard-negative Discriminative Generation Hashing for Cross-modal Retrieval
Authors: Kun Cheng
Qibing Qin
Wenfeng Zhang
Lei Huang
Jie Nie
Venue: ICLR 2026 Poster (2026)
Keywords: Deep Hashing
Cross-modal Retrieval
Informative Learning
Hard Negative Generation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Hard negative generation (HNG) provides valuable signals for deep learning, but existing methods mostly rely on local correlations while neglecting the global geometry of the embedding space. This limitation often leads to weak discrimination, particularly in cross-modal hashing, which obtains compact binary codes.
We propose Deep Global-sense Hard-negative Discriminative Generation Hashing (DGHDGH), a framework that constructs a structured graph with dual-iterative message propagation to capture global correlations, and then performs difficulty-adaptive, channel-wise interpolation to synthesize semantically consistent hard negatives aligned with global Hamming geometry.
Our approach yields more informative negatives, sharpens semantic boundaries in the Hamming co-space, and substantially enhances cross-modal retrieval. Experiments on multiple benchmarks consistently demonstrate improvements in retrieval accuracy, verifying the discriminative advantages brought by global-sense HNG in cross-modal hashing.

Published as a conference paper at ICLR 2026
DEEP GLOBAL-SENSE HARD-NEGATIVE DISCRIMINA- TIVE GENERATION HASHING FOR CROSS-MODAL RE- TRIEVAL
Kun Cheng', Qibing Qin
Wenfeng Zhang", Lei Huang". and Jie Nie*
QQufu Normal University, Rizhao, China, yuebai edu. en Weifang University, Weifang, China, qibing@wfuu edu. cn Chonggiin Normal University, Chongqing, China, i ttzzangwf@cqmm eau...edd.com on "Ocenn University of China, Qingdao, China, [huangl, niejie @ouc. edu. on
ABSTRACT
Hard negative generation (HNG) provides valuable signals for deep learning, but existing methods mostly rely local correlations while neglecting the global geometry of the embedding space. This limitation often leads to weak dis- crimination, particularly in cross-modal hashing, which learns compact binary codes. We propose Deep Global-sense Hard- -egative Discriminative Generation Hashing (DGHDGH), framework that constructs structured graph with dual- iterative message propagation to capture global correlations, and then performs difficult--adpptive, channel-wise interpolation to synthesize semantically consis- tent hard negatives aligned with global Hamming geometry. Our approach yields more informative negatives, sharpens semantic boundaries in the Hamming co- space, and substantially enhances cross- modsl retrieval. Experiments mul- tiple benchmarks consistently demonstrate improvements retrieval accuracy, verifying the discriminative advantages brought by global-sense HNG in cross- modal hashing. Related code and data are available at https://githien com/QinLab- WOOb--FUU/RD DGDDH
Parrfal-sense Interpolation
Global-sénse Interpolation
Figure 1: Traditional generation methods only interpolate based the correlation between single anchor-negative pairs, which damages the global distribution relationship of heterogeneous sam- ples in the embedding co-space. Through the interpolation of hard negative samples with global awareness of sample correlation, the generated samples are controlled to avoid violating the feature distribution in the embedding space, which makes the co-space more discriminative.
INTRODUCTION
Deep Cross-modal Hashing Retrieval (DCHR) aims to learn deep hash functions that project het- erogeneous samples into compact hash codes within shared Hamming embedding space, such that semantically similar heterogeneous samples are assigned similar codes, and dissimilar ones are mapped to distinct codes Hu al. (0023); Zhang
(2023): Liu al. (2019). This property transforms cross-modal retrieval into a simple and efficient hash based search Luo et al. (203))
Han et al. (026)) Li 2025b Qin (2025)
1 CCorreppnndigg Author.

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To enhance discriminability, one effective strategy is to provide more informative signals during training Rubinstein et al. (1997); Cakir et al. (2019). Informative learning methods can generally be categorized into mining based and generation-
approaches Wu et al. (2017) : Peng al. (2024). Currently, hard negative mining is the most widely used strategy Wang et al. 2025); ) Xuan et al.
(2020a). Difficult samples provide stronger adversarial signals. yield larger gradient updates, and force the model to learn more discriminative representations Kalantidis et al. (2020); Cheng et al. (0226;; Xia et al. (2022). However, mining is constrained by the scarcity of naturally occurring hard samples within each mini-batch, limiting its effectiveness during training Zheng et al. (2019); Zhang al. (2022): Vasudeva al. (202T). Hard negative generation (HNG) addresses this issue by synthesizing more challenging samples, typically through linear interpolation of existing negatives,
thereby enriching informative learning Peng et al. (2024) Yang et (2023).
Despite these advances, most existing works focus solely on local neighborhoods for negative inter- polation, failing to capture the global geometric structure across diverse classes, an issue particularly pronounced in the cross-modal co-space. As shown in Fig. [ select distant negative samples and create harder negatives based solely on anchor-negative corre- lations. For example, when selecting blue text embeddings as negatives for purple image anchor, the interpolated sample may mistakenly fall into the red category distribution. This failure arises because local interpolation ignores the influence of other categories and the overall global distri- bution. Consequently, generated samples often intrude into non-original semantic regions, thereby weakening discriminability.
traditional interpolation strategies
To overcome this issue, we propose learning global sample correlations and explicitly modeling inter-class relationships during generation, enabling the synthesis of informative negatives with ap- propriate difficulty that respects the semantic manifold. Specifically, we introduce Deep Global- sense Hard negativee Discriminative Generation Hashing (DGHDGH), which performs Discrimina- tive Global-sent sense Synthesis (DGS) guided by Relevance Global Propagation (RGP). In the RGP, we construct a structured graph where nodes store embeddings and edges encode pairwise relevance. Through iterative message propagation, each edge learns global-sense correlations. The DGS then uses these correlations to perform channel-wist adaptive interpolation, cnsuring the generated sam- ples remain semantically consistent. Unlike traditional methods that apply single coefficient across all channels Ko & Gu 20 Venkataramanan et al. (202)). our approach adapts difficulty per chan- nel, with additional self-paced mechanism to regulate generation hardness throughout training. Moreover, no extra generator network is required, improving adaptability and efficiency.
In summary, the main studies of this paper are listed as shown below.
Firstly, we propose a novel DGHDGH framework, which is the first attempt, to the best of our knowledge, to introduce hard negative generation into cross-modal hashing- By learning global sample relevance and synthesizing hardness-adaptive negative samples, DGHDGH achieves more discriminative cross-modal retrieval.
Secondly, we devised the RGP module, which uses graph neural networks establish global heterogeneous sample correlation perception in order to determine the appropriate difficulty of synthetic negatives and enhance the semantic alignment of synthetic samples in the co-space.
Thirdly, we designed the DGS module flexibly generate channel-wise hardness adaptive negatives based on global relationships, thereby enhancing informative hash learning.
Finally, extensive experiments on three henchmarks demonstrate that the proposed DGHDGH learns discriminative Hamming co-space through informative hash learning with global-sense HNG, surpasses state-of-the-art methods in retrieval performance, and can serve as a plug-and-play module to enhance existing cross-modal hashing approaches.
RELATED WORKS
Deep Cross-modal Hashing Retrieval (DCHR) has been extensively studied for aligning heteroge- neous modalities in a shared Hamming space Chen et al.] (2023): Li et al. (2023). Early works primarily emphasized supervised semantic alignment, while more recent approaches introduced hierarchical structures, neighborhood- prreetovd--reeriigggg mechanisms, or uncertainty estimation to enrich
training signals Li et al. (2025c): Qin al. (2024); Huo al. (202hb). Despite these advances,
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Figure 2: The schematic of our proposed DGHDGH framework. (1) We employ dual transformer architecture with hash layers to extract hash codes from heterogeneous data synchronously. (2)
RGP represents codes of the entire batch by graph and introduces iterative graph message propagation mechanism via another dual transformer that updates nodes and edges alternately. (3)
DGS uses the learned global relevance to produce interpolation vectors for each anchor-negative pair to get a harder version with discrimination.
most methods still rely on fixed training pairs and lack mechanisms for generating informative hard negatives, which constrains their discriminative capability Duan et al. (0188: Zheng et al. (2019).
Existing approaches to informative learning can be broadly divided into two families. Mining-based methods explicitly select particular forms samples to maximize the extracted information, such as Distance Weighted Sampling (DWS) Wu et al. (2017). Augmentation based methods instead create additional supervision signals, including generator-basee
approaches such GANs, interpolation- based strategies like Dense Anchor Sampling (DAS), and memory-based mechanisms like Cross Batch Memory (XBM) Cao et al. (2018b); Liu et al. (2022); Wang et al. (2020b).
While hard negatives play a crucial role in improving model discrimination, the effectiveness of hard negative mining is often limited by the number of available samples Bucher et al. (2016); [Xuan et al. (020a). Hard negative generation (HNG) has therefore emerged as promising alternative. Most existing methods obtain relationships through interpolation or generate features via a separate gen- erator, but they generally focus only on local correlations, which can distort semantic consistency. For example, Hardness-adaptiiv Deep Metric Learning (HDML) Zheng et al.] (2019) synthesizes samples based on local neighborhoods, yet fails align the generated negatives with the global geometry of the embedding space [Peng et al.) 2024). To address this limitation, we propose a novel HNG framework tailored for cross-modal hashing. Our method leverages global feature perception to generate hardness-adaptive negatives that better preserve semantic alignment across modalities, thereby enhancing discriminative retrieval. An extended discussion is provided in Appendix A
3 METHODOLOOGY
3.1 FEATURE EXTRACTION
schematic the proposed DGHDGH framework shown Fig. 2 Let xf and x denote image and text modality samples from a multi-modal dataset D = -/. i, 1. Semantic features iiz and AT of length K are obtained through the hash functions pz and FT. Here, E 0, 1 NxC is the common multi-hot label vector for the i-th heterogeneous pair (x, xx! where N denotes the number samples and C' the number of categories. To generate hash codes, we adopt Transformer- based feature extraction by employing dual Transformers for the image and text modalities. Each Transformer contains N₁ blocks followed by a hash layer. A block consists of a Multi-head Self- Attention (MSA) module with M, heads and Multi-Layer Perceptron (MLP), separated by Layer Normalization (LN) and equipped with residual connections. The hash layer consists of an MLP followed by tanh activation. Since binary optimization prototypical NP-hard problem, the
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tanh function is used continuous relaxation strategy to learn binary-like codes during training.
h lanh(MI.P(=Mm))) E (-1,1))XX,
(I,
(1)
During testing, the sign function is leveraged to obtain binary codes:
h" sign(hi) E 111, E (I,7)
In the following sections. we omit the superscripts Z, T, when the modality distinction is not critical.
where z denotes the features learned by the Ni-th block. For the features 2 learned in the k-th block, the update rule is:
LN (MLP(2)) + +2). where 2 = LN L +
(3)
Through this process, semantic- preserving hash codes can be effectively learned. However, the resulting codes still suffer from insufficient discriminability. To address this, we introduce a global- sense hard negative generation method to enhance training informativeness, consisting of two mod- ules: Relevance Global Propagation (RGP) and Discriminative Global-sense Synthesis (DGS).
3.2 RELEVANCE GLOBAL PROPAGATION
To effectively generate information-rice hard negatives, it is crucial determine both their appro- priate difficulty level and spatial distribution. Thus, when selecting interpolation points for each anchor-negative pair, their similarity should be evaluated relative all other samples in the global batch context. To this end, we construct a structured graph to capture sample associations across the entire batch and employ a graph network to learn global correlations.
Initially, we assign the batch features h into the structured graph 9 = (V, F.) as nodes V = 0 h4. Edges E represent pairwise correlations, initialized as Eij k== = h, @ hy. We maintain three graphs in parallel: image, text, and cross-modal. The first two take samples from their respective modalities, while the cross-modal graph contains all heterogeneous samples. We then introduce a graph transformer (GT) with N₂ blocks and M2 heads for each block, to learn sample relationships globally via iterative message propagation. The three graphs share parameters and are jointly up- dated GT, which helps narrow the cross-modal semantic gap and improves robustness. Message propagation adopts trau-trrasformer architecture that updates nodes and edges separately. Unlike the synchronous feedforward dual Transformer in feature extraction, the node and edge Transform- ers here perform asynchronous alternating updates- ffrstt propagating node messages, then edge messages. This ordered procedure ensures that node information continuously informs subsequent edge updates, thereby improving the model's ability to capture and exploit global sample correla- tions.
For the node Transformer, we design Masked Multi-head Self-Attention (MMSA) mechanism with a positive mask, which ensures that each node (treated as an anchor) interacts only with its negative samples. In MMSA, each node is treated query, and all corresponding negative samples treated as keys and values. To prevent disproportionately high attention weights from weakening discrimination among subtle negative differences, positive samples masked especially hetero- geneous identical samples in the cross-modal scenario. We further introduce edge-to-node interac- tions after MMSA, incorporating neighboring edge information into nodes to enrich representations and strengthen global context understanding. The main formula of the k-th node transformer block shown as follows:
V++ = LN L.LLN(MMLNN (AN(MM((V))))) + V.). where V/ = LN =LNMMMSA(V*) +
Σ j=1
Ek + V+).
(4)
For the edge Transformer, we introduce node-to-edge interactions via Cross-Attention (CA) mech- anism. Here, edge representations act as queries, while node representations serve as keys and values, allowing edges to integrate information from neighboring nodes. This allows edges to cap- ture the relevance of their critical points from a global perspective and further adjust their attention trends, thereby enabling edges to adaptively balance the difficulty of synthesizing anchor-negatee pairs. The formula of the k-thh edge transformer block is shown as follows:
E = LN (MLP(E')) + El.). where Eyy = LN (CA(E)) V ; vk+2) + Ett). (5)
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After n2 iterations of message propagation, i.e., n2 tral-trrasformer blocks, the edge information expected to encode sufficient global correlations to enrich the information content of synthetic negatives.
A more formal discussion of the propagation behavior of RGP, including how it differs from classical smoothing-basee graph operators, is provided in Appendix B
3.3
DISCRIMINATIVE GLOOBAL-SENSE SYNTHESIS
Based on the learned global sample relevance, we dynamically interpolate and fuse anchor anchor-negative representations with channel-win adaptivity, producing more informative negatives that enhance training and strengthen the discrimination of the embedding space. Initially, use the edges E of each anchor-negative pair obtain the corresponding interpolation vector Xani
(6) where FC denotes a Fully Connected layer used for transformation, and the Sigmoid function per- forms normalization. Thus, Aan can provide adaptive weights for channel-level embedding fusion in the corresponding anchor-negative pairs.
Xan Sigmoid(FC (FC(E
Unlike traditional interpolation methods that apply single coefficient, gradually increase train- ing difficulty as the model converges. Therefore, we define the interpolation formula as follows:
Kan (1 (1-1)) h) + mphn if dap < dan, where rj = (dap - A dap) otherwise.
/dan)/ddannn /dan
where is introduced as dynamic scaling factor for adjusting interpolation points, gradually in- creasing the difficulty of synthesizing negative samples during model training. We set r = e tang where lazg is measured by the average loss from the previous epoch, reflecting the model's cur- rent learning performance. As the model gradually fits, lang decreases, and T gradually tightens the upper bound of the interpolation interval, increasing the difficulty of synthetic negative samples. Ann responsible for generating appropriate deterministic values within the interpolation interval achieve informative interpolation based on global propagation of correlations.
3.4 GENERATION OPTIMIZATION
To optimize the generation of difficult samples, we design multiple loss functions that guide the model toward the desired objectives from different perspectives. We expect the generated samples to have a higher similarity (difficulty) with the anchors while maintaining the original semantics, so designed two losses: Semantic Preservation loss Cap and Interpolation Similarity loss Cts. To calculate the semantic preservation loss, we add an extra classification layer to the model. This layer trained only on real samples and then used to classify synthetic samples, without backpropagating gradients from the synthetic inputs. The calculation formula of Lap is as follows:
Chan) CE(CL(N (.)
(8)
where CL denotes the classification layer, which is essentially FC layer. CE is the cross entropy function, and ln is the original negative sample category.
For the Li., directly use cosine similarity function to calculate:
Wan 3 ha
Lis (N ha) =
Ihaall ||hall
To encourage diversity among synthetic negatives, the interpolation coefficients À should vary across pairs. Thus, for each anchor a, the standard deviation of all associated coefficients X defines the Coefficient Diversity loss Led:
Lea(>a-) = 1 (
(10)
where represents the standard deviation function (std).
The overall Generation Optimization loss Lyo is defined as:
Ego Melio + Yeplap + Yoalod
(1I)
where Yie. Tep, and Yia used to adjust the weights of the loss items. Through comprehensive sessment of three aspects, we enhance the model's ability to generate informative negative samples, thereby enabling more discriminative hash learning.
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3.5 HASH LEARNING
After obtaining diverse synthetic samples, we focus on strengthening discriminative hash learning while maintaining robustness. Since we designed classification layer the generation optimization section to evaluate the semantic consistency of synthetic samples, we need to add this layer after the hash layers and train using the corresponding loss:
Lapl (EECLI(H)),1)
(12)
At the same time, in order to maintain semantic consistency the graph neural network, we also pass the node representations after graph message propagation through a classification layer, so that the nodes continue to maintain their semantics while learning global information:
spp = CV," 4)
(13)
Note that these two classification layers differ from the modality-spedne specific hash layers; instead, they share parameters across modalities, similar GT, enhance robustness. This is because aim for the feature codes obtained from the hash layers to already eliminate modality differences, thereby allowing them to be directly applied during testing.
We adopt the standard triplet loss for hash learning, incorporating both real and synthetic hard negatives verify the effectiveness. We first compute the triplet loss using real samples only:
Lreat Liiihh ( Lmn (ich,,ii ,h,) Lmi (i", +
(14)
where Ci represents the triplet loss function. We then introduce the synthetic hard negative samples generated by our DGS module to further strengthen the learning process. The enhanced triplet loss with synthetic negatives is defined as:
Layn Lii T. Lui (nt Lun(h" Lnn(h" Ln ,KT2')
(15)
where hT ÂTTI TTI TTT represent the synthetic hard negative samples generated for the respec- tive modality pairs, which are interpolated by Eq. 7 Among them, ATT! represents the synthetic samples with I as the anchors and TT as the negatives.
The overall hash learning loss Lu combines the real and synthetic triplet losses:
Lu Lreal
Yyya
(16)
where Tsyn is set to 1 e A As GT converges, it progressively increases the proportion of hard negatives to strengthen metric learning.
The overall training procedure alternates between Lgo and Lut ensuring that both sample genera- tion and hash code learning are jointly improved throughout the training process. This coordinated optimization strategy enables our model learn highly discriminative hash codes that effectively preserve semantic similarities across modalities.
4
EXPERIMENTS
4.1 BENCHMARK DATASETS & BASELINE METHODS
MIRFLICKR-25K contains 24,581 image-text pairs across 24 semantic categories from the Flickr website Huiskes & Lew 2008). NUS-WIDE was constructed by the National University of Sin- gapore, contains 195,834 pairs, 21 classes CCha et al. (2009). MS COCO created by Microsoft, contains 122218 sample pairs from 80 categories Lin et al. (2014). In our experiments, those three datasets are split identically by randomly selecting 10,000 image-text pairs as the training set. Af- terwards, 5000 pairs are chosen randomly the query set and the remaining as the database.
To demonstrate the performance our method comprehensively, we have chosen several typical deep cross-modal hashing methods to compare with our proposed DGHDGH framework, which include Two-step discrete hashing (TwDH)Tu et al. (2024). ). Deep Neighborhood-awe -wwrre Proxy Hash-
ing (DNPH)Huo et al. (2024a), Deep Neighborhood www.ttovvidrreeeeeeeeeeee Hashing (DNpH)Qin et al. (2024), Deep Hierarchy-awe aware Proxy Hashing (DHaPH]HHu et al. (2024b), Bi-Direction Label-Guided Se- mantic Enhancement Hashing
al. 2025). Deep Evidential Hashing (DECH) Li

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Table 1: MA Polll results(9:) of DGHDGH and haseline methods on three benchmark datasets w.r.t. four hash bits.
MIRFLICKR-25K
NUS-WIDE
MS COCO
Reference
64
64
128
TwDH
TMM 24
79.71
81.47
83.19
84,37
66.83
69.34
69.95
71. 94
64.29
70.04
73.08
75.
DNPH
82.89
DNpH
TMM 24
84.23
85.52
85.88
86.29
69.21
70.22
70.71
71.58
67.27
69.05
68.60
68.74
Image
DHaPH
T4DE
82.99
84.37
85.31
85.49
69.58
20.35
71.36
71.55
72.84
74.15
74.75
75.43
BiLGSEH
TCSVT'25
79.29
81.16
81.94
82.07
70.50
71.42
72.18
72.13
66.68
73.33
75.96
74.85
Text
DECH
AAAU'25
79.61
K3.96
83.83
84,43
66.13
71.61
71.55
72.41
63.73
64.35
66.44
68.49
64.77
DDBH
TCSVT
$4.50
65.34
86.10
86.50
69.34
71.45
72.29
72.29
71.65
74.54
76.81
78.24
DGHDGH
OURS
87.75
72.06
74.71
79.19
TwDHI
TMM'24
77.80
80.01
81.96
82,96
67.06
71.02
71.37
72.60
65.68
70.92
74.45
76.
DNPH
82.32
DNpH
TMM'24
81.47
82.92
83.61
84,22
69.92
71.37
71.39
72.31
65.62
68.60
69.28
68.87
Text 4
DHaPH
T4DE
81.48
81.65
82.29
82.79
68.78
70.54
69.98
70.42
69.35
70.69
71.54
71.87
BILGSEH
TC
80.48
82.41
83,43
83.47
70.27
70.89
72.02
73.24
68.96
73.16
75.43
74.72
Image
DECH
25
78.69
81.85
82.23
83.67
68.28
73.05
73.15
73.18
62.11
65.27
66.97
69.15
83,55
63.49
62.56
64.23
71.75
DDBHI
TCSVT
$2.45
83.18
83.90
84.33
70.23
72.11
73.25
73.53
71.67
73.94
75.95
77.07
DGHDGH
OURS
83.03
84,21
85.09
85.74
70.75
72.64
73.75
74.64
71,16
74.69
77.41
79.59
The best and second-best performance bighlighted boldface and underlined.
Figure 3: Performance comparison with augmentation-basee methods on MIRFLICKR-25K and use mining- based w/ DWS as the gray background.
et al. (2025c), Deep Probabilistic Binary Embedding (DPBE) Cheng al. (2025) Deep Discrimi- native Boundary Hashing (DDBH)Qin et al. (2025) To ensure fairness, all frameworks adopt CLIP VIT-B/32 as the common backhone, and the experimental settings kept the
except for the hyperparameters set in the original paper. Furthermore, different types of informative methods are picked, namely -eeighee Sampling (w/ DWS) Wu et al. (2017). hashGAN (w/ GAN) Cao et al. (2018b). Hardness-adaptive Deep Metric Learning (w/ HDML) Zheng el al. (2019) and Densely-Anehh Sampling (w/ DAS) Liu al. (2022).
4.2 EVALUATION METRICS & IMPLEMENTATION DETAILS
We evaluate cross-modal similarity search in two settings: Image-to-Tex (I2T) and Text-to-Image (T2I). We primarily use mean Average Precision (mAP), which reflects both recall and precision, along with the Fisher ratio and PQH < 2 evaluate model discriminability.
The initial parameters the feature extraction module are referenced in Radford et al. (021). where Ni 12, Mj 8. For parameter optimization, we utilize the Adam optimizer, where a learning rate of 0.001 and a weight decay of 0.01. We set the batch size as 128 and take the best performance in 100 epochs for all experiments. A detailed description be found in Appendix C
4.3 PERFORMANCE COMPARISON
To rigorously verify the performance of our proposed DGHDGH, we report the comparison with baseline methods as shown Tab. By learning a discriminative Hamming co-space, our method achieves Mate-oo--hhean performance results. Meanwhile, in order to comprehensively demonstrate the difference with previous information learning, we further compare DGHDGH with representa- tive generation methods on MIRFLICKR-25K, baseline. Other metrics are also evaluated in a normalized radar plot, which are Flops, Parameters (reverse), Training times (reverse), convergence epochs (reverse), and information augmented. The metrics were all performed Max normalization, and the maximum value was taken as the largest or slightly larger constant. See more baseline experiments in Appendix D.I and D.2
shown in Fig. 3 which are all based the same

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Table 2: Fisher ratios (%) of DGHDGH and baseline methods w.r.t. four hash bits on three benchmark datasets, which are computed by stability.
rundomil sampling 200, postiveheeaative pairs, each repeated times with
16 bits
32 bits
64 bits
128 bits
MIRELICKR
I-37
T-+f
7-+/
1-+7
7--s
DHaPH
90.15
84.38 1 0.19
204,54
88.16 & 0.07
2
90.89 0.21
108.28 a 0.18
92.46
0.08
BILGSEH
0..11
09.05 + 0.06
76.41=0.17
75.86 0.06
80.42 0.19
82.62 + 0.07
81.36 0.18
34.46 0.08
DECH
0.113
67.85 0.06
96.38
80.76
0.11
91.55
87.98
0.13
DDBH
100.13
104.5500.11
111.30 0.10
92.47
DCHDGH
27.57 0.08
89,02 = 0.14
105.44 0.08
93.16 0.11
111.17
94,60 = 0.05
110.17
96.82 + 0.15
NUS-WIDE
D.13
0.16
0.14
0:13
BILGSEH
0.13
101.8 0.111
100.32 0.21
102.42 0.17
99.00 0.18
103.24 0.14
99.00
0.18
103.21 0.13
DECH
0,19
97.85 + 0.13
105.56 0.14
108.89 0.11
97.69 0.15
105.49 0.10
104.69 0.13
109.24 0.09
DDBH
0.17
110.02
0.17
01212220.17
111.14+0.5 0.15
0.11
113.42- 0.14
116.49
0.15
0.15
DGHDGH
105.03
104.6800.10
= 0.22
1 0.12
0132770.18
114,41 0.07
0.13
0.09
MIS COCO
DHaPH
11694 0.17
0.04
113.73 0.14
114.38
117.05 0.10
120.65 0.47
BİLGSEH
0.08
DECH
9150 D.SS
94,46 0.12
105.41 + 0.11
95.77 0.10
108.17 0.10
98.15 0.12
107. 36 0.11
DDBH
123.3 0.17
08
131,57 1 0.15
123.37 0.11
136.25 +
124.64 0.11
139,.0 0.20
126.46 0.10
DGHDGH
The best and socodd-best performance are highlighted in bokllave and underlined.
Figure P@H < results of DGHDGH and baseline methods on three benehmark datasets.
4.4 DISCRIMINATIVE HASHING
We argue that introducing richer discriminative information during training facilitates more discrim- inative hash learning. To evaluate this, P&H < 2 is utilized to demonstrates the compactness of the learned Hamming co-space. In Fig. 4 the experimental result measures how well each model pushes away hard negatives, validating the discriminative capability of our proposed method.
On the other hand, assessed discrimination by comparing the Fisher ratio. As shown in Tab, 2 our method achieves higher Fisher ratios than all baselines. This indicates that the proposed global- sense hard negative generation leads to more discriminative Hamming space, leading tighter intra-class clusters and larger inter-class separations. It is worth noting that the two methods that performed well in DDBH, similarly emphasize discriminative properties.
4.5
SELF VALIDATION
To fairly judge the contributions of our modules, we conduct ablation studies evaluate each com- ponent separately in Fig. 5 For w/o RGP, we directly use the initial edge computation as the interpo- lation source. For w/o DGS, we directly remove the generation phase. Furthermore, we validate two detailed operations in two modules. Furthermore, we validate two finer operations in the modules: removing Edge Message Fusion (w/o EMF) in RGP, and removing the HardnesssAdaativ Parame- ter (w/o HAP) in DGS. At the same time, we also investigate the three optimization objectives for generative embedding, i.c., CispCep and Led, and cross ablate them in Tab. 3 These three loss terms
22
Figure 5: Ablation Study Result of DGHDGH three benchmark datasets w.r.t. 128 bits.
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Triniig Time
Encoding Time
D081
205
1001
5.004
Figure 6: Parameter configuration and temporal effects Results on MIRFLICKR-255 W.L.t 16 bits.
Table 3: Ablation Study Result of DGHDGH on MIRFLICKR-25K,
Componeat
bits
32 bits
64 bits
128 bits
Avg.
Eap
I-T
T-l
[->7
T
[-T
T-
I-T
T-1l
I-T
T-1l
79.82
78.15
81.45
79.63
82.21
80.57
83.06
81.32
81.64
80.04
81.38
79.52
82.97
80.73
83.84
81.69
84.61
82.58
83.20
81.13
82.15
80.37
83.82
81.64
84.59
82.81
85.33
83.45
83,97
82.07
83.76
82.14
K5.41
83.28
86.25
84,36
57.02
84.95
85.61
83.68
83.92
82.35
85.63
83.51
86.47
84.59
87.21
85.17
85.81
83.90
V
84.44
82.91
86.09
83.95
86
84.98
38
85.43
86,18
84.32
84.66
83.03
86.19
84.21
87.13
85.09
87.75
85.74
86.43
84.52
The best and second-bes best performance are highlighted in boldiace and underlined.
optimize generated hard negatives in terms of interpolation similarity, semantic preservation, and parameter diversification, respectively. The figure demonstrates that optimizing the interpolation process from all three perspectives leads to better results. More analysis in Appendix D.3]
We further conducted hyper-parameter experiments validate the choice the number of blocks N₂ and the attention heads M2 in the graph transformer, and selected sets of configurations as conl, con2 ..., were compared with baseline methods in terms of training time and encoding time. Training time is measured over 100 epochs (hours), and encoding time is measured for single pass over the query set (ms). These experiments are shown in Fig. 6 To combine performance and efficiency, we chose conl as the final parameter, i.c., N2 2, M2 4.
4.6 MODULE GENERALIZATION
Our proposed method serves as an information-rich strategy that provides broad support for cross- modal training. To validate this, we extend it to the discriminative approach DHaPH and DDBH. As shown in Fig. 7 our method can be used in a plug-and-play manner to support various approaches. Furthermore, to validate the capacity of augmentation- based methods to cope with www-iformation training challenging environments, We first halve the train set size and then halve the batch size consecutively. Considering the instability in this scenario, we perform multiple experiments and record the variance as shown in Fig. g Our method can still stably provide discriminative infor- mation to support training in the face of fewer samples. We visualize the distribution of negative samples before and after the proposed method generates difficult negative samples in Fig. We also checked the stability of the backbone in Appendix D.4
SHAFT
TERRI
938
932 PIO
Figure 7: Bullet chart visualization on MIRFLICKR-25K, The target markers indicate the baseline and bars correspond add the DGHDGH module.
4.7 NOISE ROBUSTNESS
We conducted noise label experiments verify the performance degradation of the model when countering label interference. Noise rates 0.2, 0.5, and 0.8 indicate random two-digit label in- version of the corresponding proportion of samples during training, follow in Wang al. (2024)

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Published as a conference paper at ICLR 2026
Figure 8: Batch stability error with line plots for different training set sizes (5000, 7500). Batch size taken as 32, 64, 128, 256.
A5
Figure 9: Visualization of the distribution of relative distances of negative samples before and after generation, and their cosine similarity histograms.
DGHDGH maintains stable retrieval performance at all noise levels and consistently outperforms all baselines. Graph propagation aggregates the relationship signals of multiple adjacent samples and dominates the propagation process with global relationships, forming a natural denoising filter that suppresses the influence of damaged labels. Meanwhile difficulty- aware synthetic negative samples generate hard negative samples based on cross-modal similarity and structural consistency, without relying on original labels, thus avoiding the amplification of noise and the generation of misleading negative samples by erroncous labels. Because synthetic negatives does not depend on noisy labels but on learned similarity and global propagation, it is inherently more robust than baselines.
Table 4: mAP results (%) of DDGRH and baselines under different noise rates on the MIRFLICKR-255 dataset w.r.t.
four bits.
bits
bits
bits
128 bits
Task
Method
0.2
0.5
0.8
0.5
0.8
0.5
0.8
0.5
0.8
NRCH
76.38
75.61
73.24
76.75
76.21
74.76
77.05
76.98
76.07
77.83
77.10
76.
DNPH
77.70
75.55
75.37
79.74
80.16
79.04
81.67
82.44
81.01
83.53
83.32
81.08
Image
DHaPH
83.73
81.64
78.04
82.94
78.11
75.82
82. 10
79.17
76.85
82.64
79.77
77.28
4
BiLGSEH
78.11
77.35
75.93
78.24
77.36
75.15
80.72
78.42
76.30
80.00
79.32
77.46
Text
DPBE
77.85
73.79
77.16
74.09
78.28
75.45
80.11
76.77
DDBH
83.79
82.20
76.50
84,70
82.11
78.78
85.04
84.58
79.46
85.64
84.61
80.29
DGHDGH
81.95
81.78
78.65
84.06
82.76
80.89
85.96
84.71
82.64
83.96
85.15
84.16
NRCH
74.55
74.31
72.20
75.53
74.68
72.59
7.5 88
75.41
74.59
7.5.71
75.80
74.64
DNPH
76.40
75.18
78.56
79.28
77.05
80.56
79.25
81.26
80.76
79.45
Text
DHaPH
81.51
79.49
77.66
80.52
75.96
73.22
78.69
76.39
74.23
79.31
76.99
74.38
BiLGSEH
77.23
76.61
76.30
81.27
77.85
72.16
79.21
75.67
75.14
79.55
77.36
76.72
Image
DPBE
76.26
75.82
73.29
78.83
76.28
74.38
81.17
78.13
75.76
82.59
80.14
77.22
DDBH
82.16
80.27
77.07
32.34
81.22
79.62
24
82.79
79.77
83.76
82.69
79,84
DGHDGH
81.03
80.42
78.80
82,59
81.67
80.87
84.56
83.50
82.09
84.56
84.13
83.35
The best and socond-best performances are highlighted in boldface and underlined.
5 CONCLUSION
In this work we presented DGHDGH, the first framework that introduces discriminative generation into deep hashing. By combining global relational modeling with hardness-adaptiiv synthesis, our method generates semantically consistent negatives that sharpen decision boundaries in Hamming space. Extensive experiments on multiple benchmarks verify that DGHDGH significantly improves retrieval accuracy and discriminability formance, our framework is modular and can serve as a plug-and song-and-play enhancement for existing cross-modal approaches,and and will be accessed for arbitrary representation learning in the future.
ofte-off-the-n art baselines. Beyond its standalone per-
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ACKNOWLEDGEMENT
This work was supported by the National Natural Science Foundation of China (No.62302338) Shandong Province Natural Science Foundation (No. 2N0222202200466, No.2R2025MS 10677), Natural Science Foundation of Chongqing (No.CSTB200 (N.CCTB2222NNCONNNXXO077 and Science and Technology Research Program of Chongqing Municipal Education Commission (No. (JQN2220000)))
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A RELATED WORK
This section provides the detailed discussion of related works that were briefly summarized Sec- tion 2 of the main paper.
A.1 DEEP CROSS-MODAL. HASHING RETRIEVAL.
Deep Cross-modal Hashing Retrieval (DCHR) aims to map heterogeneous modalities.such as im- ages and text, into a collaborative Hamming space end-to-end for efficient approximate nearest neighbor retrieval Chen et al. (2023). With the benefit of low storage cost and high retrieval effi- ciency, DCHR methods have attracted wide interest in the field of cross-modal retrieval, achieving superior similarity retrieval performance Li et al. (2023); Zhu et al. (202)). Deep Cross-Modal Hashing (DCMH) uses negative likeliiildood loss to maximize the similarity of hash codes for similar samples and minimize it for dissimilar samples Jiang & Li (2017). Self-Supervised Adver- sarial Hashing (SSAH) uses adversarial learning and self-supervised semantic discovery to improve the alignment of multi-label semantic distributions Li al. (018a). Multimodal transformers with
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differentiable hashing mechanism leveraged by Differentiable Cross- Hashing via Multi- modal Transformers (DCHMT), enabling gradient- baadee optimization through CLIP-style represen- tations Tu et al. (002)).
In recent years, researchers have sought to overcome the limitations of conventional cross-modar hashing by incorporating more informative learning strategies. Deep deighborhood-preseenee Hashing (DNpH) improves the discrimination and semantic consistency of cross-modal hashing by preserving the Neighborhood structure and combining quadratic spherical mutual information QQn et al. (202)) By introducing hierarchical agent and self-paced learning mechanism, Deep Hierarchy-awee aware Proxy Hashing (DHaPH) can gradually capture global and local hierarchical se- mantic information Huo et al. (2024b). Deep Evidential Hashing for Trustworthy Cross-Modie Re- trieval (DECH) models the uncertainty information by evidence theory, which makes cross-modal hashing more advantageous in generating trustworthy and interpretable retrieval results Li et al. 2025c).
A.2 INFORMATIVE LEARNING
A core bottleneck for cross-modal hashing is that training is easily dominated by uninformative easy samples, causing slow convergence and weak decision boundaries in Hamming space Qin et al. 2002); ; Meng et al. (02I). Informative learning tackles this by prioritizing supervision that carries higher training value, either by mining difficult instances from existing data or by generating chal- lenging instances to enrich supervision. From this perspective, The past works can be summarized as the pursuit of more informative learning and divided into two major threads, mining-hase -aaed and ingmentation-based based.
A.2.1 MINING-BASED LEARNING
At the earliest, facenet recognized the importance of mining and proposed semi-hard sampling to select informative examples for the triplet loss (Shroof et al. (013). Subsequently, various sampling modalities oriented to specific regions have blossomed. Easy positive mining approach holds that the query sample only needs to be close to the simple examples among its positive samples rather then whole positives Xuan et al. (2020b). This relaxed information filtering mechanism leads to better generalization. Hard negative mining is a major focus mining, which aims to select dif- ficult negatives that are highly similar positives Bucher et al. (2016): Simo-Serra al. (2013); Suh et al. 2019); Xuan et al. (2020a). Learning this information in a targeted manner during train- ing can enhance the model's ability distinguish between positive and negative examples. Hard example mining goes one step further by selecting both difficult negative examples and positive examples (i.c., positive examples with low similarity). A high degree of information acquisition allows the model to draw clear line between positives and negatives Rao et al. (2024); [Shrivastaval et al.) (2018). Distance Weighted Sampling (DWS) point out that the mining method is limited by the narrow area selected, and the reduction of the selected sample size affects the amount of information obtained. Extensive sampling can not only lead to an improvement in the amount of information, but also achieve better generalization by learning different distance rela- tionships, brings the same or even higher performance impact as the loss function Wu et al. (2017). Back to the cross-modal domain, Triplet-based Deep Hashing (TDH) introduces the Triplet with hard mining into hashing, so as to improve the discrimination of cross-modal similarity ranking Deng al. (2018). Hard-Negative Selection Strategy (HNSS) using the improved marginal ranking loss to examined a new strategy for hard-negative mining in cross-modal retrieval Galanopoulos & Mezaris I2.2I). This also pointed out that mining methods are limited by batch size even train dataset size and lack enough information. This leads to overfitting or sub aub-optimization in the end. In this scenario, series of methods designed to provide additional information have emerged.
al.) (016); [Smirnov
A.2.2 AUGMENTATION-BAN BASED LEARNING
Generator-based The most common generation method is the Generative Adversarial Network (GAN). After its popularity, many methods have also attempted to utilize GAN to enhance hash learning Qian et al. (202)). While emphasizing self-supervision. Self-Supervised Adversarial Hash- ing (SSAH) uses adversarial network mechanisms to enforce cross-modal consistency [i et al. (2018)). HashGAN introduce the generative attention mask and adversarial sample generation,
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improves the discriminative ability of hash representation by generating network to interfere with the discriminator [ao et al. (2018b).
Memory-basede A range of methods use memoization module like memory bank backup queue to get around the batch size limit to get more information. The embeddings of previous iterations are maintained by Cross Batch Memory and considered to be still informative in the current batch Wang et al. 20206b. Fast Podta Online Cross-Modal Hashing (FPO-CMH) facilitates efficient online cross-modal hash learning by using multimodal dual-tier anchor bank Li et al. (2025a).
Interpolation-basee Mixup proposes a linear interpolation method of input and label to generate virtual samples /Zhan et al. (2017). This approach has been widely used improve generalization and adversarial robustness. DAS reiterates the "miss embedding" problem for the mining and in- terpolates all real embeddings as anchors generate positive and negative pseudo-embeddinngs Liu
et al. 2022).
Hard Negative Generation To targeted acquisition negative samples with larger amount of infor- mation, the hard negative generation (HNG) can be regarded as special direction. Deep adversarial metric learning (DAML) synthesizes simple negatives into hard negatives through adversarial train- ing[Dun et al. (2018). Hardness-aware Deep Metric Learning (HDML) performs hardness-ave interpolation between anchor-negative pairs and then uses an autoencoder to generate correspond- ing features Zheng et al. (2019). A two-stage synthesis framework is introduced to generate hard positives and hard negatives at the same time Zhao et al. (2018). Most of these methods obtain sam- ple relationships through interpolation and features through generator. However, due to the inherent shortcomings of interpolation methods, difficult sample generation rarely applied to cross modall hashing, the synthesized difficult negative samples also lack spatial feature perception, which is useless or even interferes with cross-modal semantic alignment.
Motivated by these limitations, we propose novel method for generating difficult samples that can be applied to cross-modal hashing, which can assist in cross-modal semantic alignment by obtain spatial feature perception.
B THEORETICAL INTERPRETATION OF RGP PROPAGATION
The behavior of RGP propagation can be understood by comparing its update rule with the classical graph smoothing models. Standard GNN propagation is often approximated by a Laplacian operator of the form:
H =HH() + +(1 (1
(17)
which has been formally shown to act as a low-pass filter on graph signals and to cause feature convergence across connected nodes depth increases. This phenomenon has been documented in several analyses of message-passing networks, including studies of over-smoothing where node em- beddings tend to collapse into indistinguishable representations when eigenvalues the propagation operator suppress high-frequency components. Representative discussions include the analyses of Oono & Suzuki (2019) and Li et al. (2018b), which indicate that repeated Laplacian-style propaga- tion inevitably produces smoothing behavior.
The update rule of RGP differs from this model. Let Ap denote the semantic positive graph and A denote the synthesized negative graph constructed during training. RGP applies masked attention in which each anchor attends only to nodes identified by A,, while entries Ap are suppressed. The propagation can be written as
H(+1) H + " (1))
(18)
where M,, is a masking operator that selects negative nodes and 1j is a small scale factor. Because M,, excludes positive edges entirely, the operator does not approximate Laplacian. The update term contains no averaging over semantic neighbors. Instead, it introduces a structured repulsive transformation that pushes representations away along directions defined by synthesized hard nega- tives. This behavior is closer to contrastive propagation than to classical smoothing. Unlike Lapla- cian filters that attenuate high-frequency components, the negative-aware update amplifies structural
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distinctions in the feature space because the gradient of the attention logits depends differences between the anchor and its negative set.
A direct consequence is that the eigenstructure governing the propagation is not dominated by the low-frequency bases of the graph Laplacian. The update matrix produced by masked attention asymmetric and does not share the spectrum D-1/2 A1122/ Prior analyses attention- based propagation observe that attention operators do not behave low-pass filters Verma & Zhang (2019). In the case of RGP, the combination masked attention and synthesized negatives produces directional transformation that retains discriminative structure after multiple layers. This the- oretical form explains why RGP avoids the collapse and homogeneity commonly associated with Laplacian-based architectures.
Vilters/eerma
C EXPERIMENTS SETTINGS
C.I IMPLEMENTATION DETAILS
Based on RTX A6000 Ada GPUs, we adopt the open-source PyTorch framework to implement our proposed DGHDGH algorithm and other methods Paszke al. (2019). The PyTorch version is 2.3.0. They are all performed in a unified experimental setting. The pre-training parameters of the Transformer encoders feature extraction are reference in CLIP ViT-B/32 from Radford et al. (202T) applied on all methods, which have 12 Transformer blocks, and 8 heads for each attention module in blocks. For baselines, we follow their official implementations where available, or adopt hyper-parameters from the original papers.
use Adam as the optimizer, where the learning rate are le-4 for feature extraction with hash layers, and le-5 for graph transformer. The weight decay is 0.2 and the batch size is set to 128. Besides, for image preprocessing, we resize to 224 x 224, center crop, and normalize with CLIP's default statistics. Texts are tokenized using the CLIP tokenizer with maximum length 77. We evaluate hash codes of 16, 32, 64, and 128 bits.
For parameter optimization,
C.2 EVALUATION METRICS
In this work, we mean Average Precision (mAP) that comprehensive retrieval evaluation, and fisher ratio and Precision within Hamming Radius < 2 (P@H S 2) to evaluate the discrimination of models. Moreover, Recall Curve (PR), and Top-K K Precision Curve (P@K) in the appendix to further validate the re- trieval performance of the model.
introduce Normalized Discounted Cumulative Gain (NDCG), Precision-
C.2.1 MEAN AVERAGE PRECISION
We primarily use mAP as a performance metric, which calculates the average precision of each sample in the query set retrieved from the database and then averages it again. The mAP is the average precision under different recall thresholds, and it is a comprehensive retrieval evaluation including recall and precision.Thh formula is shown as:
k Σ j=1
mAP n
APOK,, where AP,
lig.
(19)
K
When i, belong to the same category, 4, = 1, otherwise 4j = 0. rj represents the number of
relevant samples in top-j in the ranking list. is the number of query samples. In this paper, choose & all i.c. the number database samples. Among them, mAP 12T uses image modality for query and text modality for database, T21 is similar.
C.2.2 PRECISION WITHIN HAMMING RADIUS <
To directly measure retrieval quality in Hamming space, we also compute Precision within Hamming radius 2 (P@H < 2) Liu et al. (2012). For given query, this metric counts the proportion of
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relevant items among all retrieved samples whose Hamming distance to the query is less than or equal to 2. As shown in formula:
Number of relevant retrieved items within H 2
P@H S
(20)
Total Number retrieved items within H S
This metric reflects the local discriminative capability of hash codes in a compact neighborhood. A higher PÔ H < 2 means that the retrieved neighbors are more semantically consistent with the query. This reflect how effectively the learned hash codes preserve semantic neighborhood structures, and higher PH2 indicates stronger local discrimination within the Hamming space.
C.2.3 FISHER RATIO
Finally, quantitatively assess the discriminability of hash codes, we compute the Fisher ratio, which compares the separability of positive and negative pairs in Hamming space. Specifically:
0 + 2 pos
Fisher
Mnng
ppos
where o'
(21)
0'
2
where Uneg and MApo denote the mean Hamming distances of negative and positive pairs, and of is the pooled standard deviation by the std &neg and pos-
In practice, we compute Fisher ratios by randomly sampling 50, 100, 200, and 400 pairs of positive and negative examples, from the hash codes of database set after training while anchors are from query set. We repeating each sampling 5 times, and reporting the averaged results with standard deviations. This statistic quantifies the margin between positive and negative relations, and larger Fisher ratio demonstrates improved global separability and stronger discriminative structure in the Hamming space.
C.2.4 NORMALIZED DISCOUNTED CUMULATIVE GAIN (NDCG)
To further evaluate ranking quality in cross cross-modal retrieval. we report the Normalized Discounted Cumulative Gain (NDCG), which measures how well a retrieval method ranks relevant items near the top of the list. For given query, the Discounted Cumulative Gain (DCG) at rank K is defined as
K
rely
DCG =
(22)
loga (j + +1)
where rely is the relevance score of the item at rank j. The ideal DCG (IDCG) is obtained by sorting relevant items in the optimal order. The normalized form is
NDCG@K DCG@K
(23)
TDCG@K
NDCG emphasizes the importance of ranking correct items earlier in the retrieval list. A higher NDCG indicates that the model places semantically relevant samples closer to the top positions, reflecting high-quality retrieval ordering beyond binary relevance. In this work, we choice = 1000 ic. NDCG@ 1000.
C.2.5 PRECISION-RECAL CURVE
We also use the Precision Recall (PR) curve to visualize the trade-off between precision and recall across different similarity thresholds in the Hamming space. For each threshold, the retrieved set determined by selecting items whose Hamming distance to the query is below the threshold. Precision and recall computed
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TP
TP Recall TP + FN
Precision
(24)
TP + FP
where TP, FP, and FN are the number true positives, false positives, and false negatives. PR provide continuous view of retrieval behavior under varying cutoff distances, allowing us to assess how a model handles both strict and loose retrieval requirements. A curve that stays consistently high indicates stable performance across wide range of thresholds.
C.2.6 TOP-K PRECISION CURVE
To measure performance at different retrieval depths, compute the Top-K Precision curve. For each query, the precision at rank is defined as
PâK
lip
(25)
K
where li if the j-th retrieved item is relevant and O otherwise. By sweeping K from small to large values, the Top-K precision curve shows how the method behaves when retrieving only few nearest neighbors when retrieving deeper lists. This metric reflects the stability of the semantic preservation across different retrieval lengths. Higher curves imply that the learned hash codes remain discriminative regardless of the retrieval depth.
D ADDITIONAL EXPERIMENTS
D.1 COMPARISON WITH CLASSICAL METHODS ON THE STANDARDIZED BACKBONE
To further validate the effectiveness of our approach, we adapted the CLIP ViT-B/32 as the standard- ized feature extractor for classical methods published prior CLIP, which did not utilize Trans- former architectures in their network, namely Deep Cross-Modal Hashing (DCMH) Jiang & Li (2018a), Adversary Guided Asym- metric Hashing (AGAH) Gu et al.) (2019), Deep Adversarial Discrete Hashing (DADH) Bai et al. 2020), Self-constraining attention hashing network (SCAHN) Wang et al. (2020a), and add "-T" to indicate backbone replacement. As shown in Tab. [ Our method continues to deliver superior performance under this standardized evaluation, demonstrating that its effectiveness not tied to specific feature extraction techniques but rather stems from the overall learning framework and its design innovations.
(217)) Cross-Modal Hamming Hashing (CMHH) Cao
D.2 COMPARISON WITH BASELINE ON MORE METRICS
To comprehensively evaluate retrieval performance, curves, and P@ K curves, as shown in Tab, 6 Fig. I0, and Fig. [I respectivel... Experiments show that, except for some cases, DGHDGH still maintains a broad lead in these indicators. This indicates that our method clearly has better retrieval performance.
also present comparisons of NDCG, PR
D.3 HYPER-PARAMETER ANALYSIS
After the Ablation study three loss coefficient terms Tis Tep and Tods we further explore the in- fluence of different values of them retrieval performance, shown in Fig. 12 As 7is similarity constraint, the gradient naturally the largest, so the choice of the anchor scale is a natural choice to ensure the stability of the optimization. We set %s to I and adjust the other two items from 0. to 10. We applied the parameter configuration at the best performance to the experiments while Ysp and Tod 0.2.
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Table 5: mAP@eal results(Sk) of DGHDCH with classic methods on the three benchmark datasets w.r.t four hash bits.
MIRFLICKR-25K
NUS-WIDE
MS COCO
Task
Reference
Method
16
128
32
64
128
CVPR'17
DCMH
76,87
77.36
77.97
78.81
53,79
55.13
56,17
57.03
53.99
54,44
56,27
56.59
DCMHLT
82.78
84.23
84.01
85.17
63.97
66.78
68.59
68.
62. 74
64.99
67.20
67.59
18
CMHH
69.32
69.79
69.84
70.23
54.39
55.46
55.2
52.91
51.45
45.09
52.09
50.21
Image
CMHH-T
81.11
82.04
82.52
83.08
67.41
68.88
69.28
09.71
54.90
46
64,30
65.49
19MR'19
AGAH
72.48
72.17
71.95
72.82
39.45
41.07
42.58
43,58
55.01
55.15
55.18
55.54
AGAH-T
80.79
81.22
81.98
83:49
59.81
64.69
66.44
67,56
61,45
64.16
65.94
66.55
Text
1CMR'20
DADH-T
DADII
80,98
82.30
83.23
81.62
84.58
81.93
82.17
84.03
63.5
65.68
67.98
65.46
68.71
66.61
70.58
63.39
59.52
61.18
66.51
62.37
68.76
69.27
63.24
Neuco'20
SCAHN
79.55
82,48
82,97
82.88
64.63
66.16
66.45
65.49
63.76
64.75
65.19
61.49
SCAHN-T
82.13
84.32
84.87
85.31
08.29
68.01
69.13
70.23
67.72
09
70.12
69.88
Ours
DGHDGH
84.66
86.19
87.13
87.75
69.72
71.68
72.60
73.76
72.06
74.71
77.13
79.19
CVPR 17
DCMH
78.57
79.98
80.29
80,83
57.47
58.11
58.53
59.04
52.71
54.24
54.5
55.26
DCMILT
80.88
82.01
83.11
84.09
64,24
66.33
69.17
69.74
62.98
65.53
67.00
67.92
Text
IC
CMHH
CMHH-T
78.33
71.81
79.51
71.04
80.16
72.94
81.75
73.72
49,56
68.53
48.
69:67
70.04
48.2
47.5
70.46
49.
55. 33
58.17
49.3
64.81
48.89
65.52
50.15
19MR'19
AGAR-T
AGAH
79.52
70.82
80.10
71.82
82.12
73.44
82.61
74,38
43.44
65.18
39.8
67:43
43.82
68. 17.17
44.05
60.91
50.12
64.38
51.46
51.91
64.49
51.36
63.91
Image
ICMR
DADH
DADH-T
80.19
81.42
82.32
81.01
83.20
81.37
82.65
81.35
61.11
61.82
62.18
69.18
63.26
70.54
56.49
61.88
66.50
57.9
58.7
60.37
Neuco'20 20
SCAHN
78.26
80.66
80.64
80.66
65.87
66.26
66.48
66. 66.09
63.77
65.12
64.93
61.45
SCARN-T
82.38
82.55
83.06
68.97
70.18
70.49
67.36
68.52
69.57
20.05
Ours
DGHDGH
83.03
84.21
85.09
85.74
70.75
72.64
73.75
74.64
71.16
74.69
77.41
79.59
The best and second hest performances are highlighted in boldface and underiined. Neuro denates Neurocopparing
Table 6: NDCG@ 1000 results(%% (%) of DGHDGH with basclines on three benchmark datascts w.r.t. four hash bits.
MIRFLICKR-255
NUS-WIDE
MS COCO
Tisk
Method
16
32
64
128
16
32
64
128
16
32
64
128
DNpH
50,22
52.66
54.30
55.07
50.86
53.37
55.26
55.78
33.88
40.06
41.07
42.19
DHaPH
46,29
48.62
48.16
48.29
51.23
46.24
54.03
55.20
39.19
41.37
41.40
42.42
Image
BiLGSEH
47.71
50.21
51.13
51.05
54.07
54.75
56.92
57.66
46.50
50.22
50.83
50.43
4
DECH
40.89
49.34
49.46
49.29
46.40
54.11
53.23
56.32
19.89
24.92
30.58
32.41
Text
DPBE
41.56
46.72
49.67
50.99
44.29
45,25
67.66
47.08
41.96
44.62
49.81
52.10
DDBH
51.25
52.72
52.42
52.57
52.88
54.89
56.56
56.09
38.48
42.56
46.04
47.78
DGHDGH
53.26
55.19
56.11
$7.38
51.68
55,95
58,59
60.94
37.31
42.52
51.10
54.72
DNpH
42.30
45.17
46.15
47,06
49.74
50.67
52.87
52.77
33.32
40.04
41.26
42.81
DHaPH
45.07
41.32
42.12
43.45
46.63
46
48.42
49.15
40.49
86
43.62
43.32
Text
BiLGSEH
45.53
47.09
48.18
53.24
54.06
$5.07
55.93
46.43
50.46
51.59
$1.06
DECH
38.69
41.53
42,86
42.84
47.58
54.82
53.23
53.46
20.52
25.86
30.62
32.10
Image
DPBE
34.16
39.05
41.52
42.46
41.06
43.59
44.88
44.60
33.83
47.20
49.89
52.45
DDBH
42.63
43.50
44.10
44.56
51.52
$2.93
54.46
38.79
40.99
44.56
46.48
DGHDGH
46.40
47.52
49.46
51.16
51.42
53.35
56.11
57.12
36.71
46.59
52.90
56.14
The best and second-best performances highlighted in boldface and underlined.
WADER SIQAAs AN0
1000
SANGE (SNEATAA
Figure 10: Results of Precision-Recar curves on three benchmark datasets w.r.t. 16 and 32 bits.
KOK (A4%
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CIRCE
WEDOO
Figure 11: Results of Top-K Precision curves on three benchmark datasets w.r.t. 16 and 32 bits.
D.4 BACKBONE GENERALIZATION
To judge the generalization across different backbones, further investigate several CLIP architec- ture with its variant BLIP and SigL [ al.) (2022); TTschnnnn et al. (2025). As summarized in Tab. 7 we report the 12T and T21 mAp (%) MIFLICLR-25K 16 bits, along with other metries: the number of Parameters in each backbone (in Million), total Training Time for 100 epochs train- ing (in Hour), Encoding Time for the query dataset encoding (in Millisecond), and the Converge Epoch indicating the best performance within 100 epochs. These results collectively demonstrate the strong generalization capability of our proposed DGHDGH method across diverse backbone architectures.
Table 7: Various metrics of DGHDGH with different backbones the MIRFLICKR-25K datuset w.r.t. 16 bits.
CLIP
BLIP
Sig
Metrie
VIT-B/32
ViT-L/14
Res50/16
I-C-Base
I-C-Laige
Baxe-16/222
Base-32/256
Lorge-16/256
Parameters
151.3
427.6
291.0
224.7
447.2
375.2
376.9
1.5
Image-+Text
84.66
84.82
83.22
82.96
83.83
82.54
84,82
84.50
Image
83.03
83.25
82.62
83.01
81.20
80.95
83.25
84.04
Training Time
1.206
4.652
5,312
5.945
13.28
1.753
2.018
5,374
Encoding Time
27.05
37.98
32.41
72.57
126.8
34,46
31.05
47.39
Converge Epoch
22


Reviews:
[{"review_id": "NTpe0BNJFS", "reviewer": "Reviewer_CBYJ", "summary": "The authors propose DGHDGH, a cross-modal hashing framework coupling a global propagation network (RGP) with an adaptive negative synthesis module (DGS). The paper delivers strong quantitative results and clear empirical validation, but certain visualizations and terminological inconsistencies slightly hinder comprehension.", "questions": "1.\tFuture work might explore coupling DGHDGH with pre-trained large multi-modal models (e.g., BLIP-2) to test transferability.\n2.\tIt might also be fruitful to explore hybrid discrete–continuous codes instead of pure binary hashing, leveraging the same hard-negative generation principle.\n3.\tThe figures could use larger fonts and more contrast; are the authors planning visual revisions for the camera-ready version?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tDemonstrates strong performance on multiple datasets and provides meaningful ablation studies.\n2.\tThe framework is innovative in combining semantic propagation with synthetic negative mining. The method is technically coherent and easily interpretable when fully understood.\n3.\tThe framework appears extendable to other multi-modal applications.", "weaknesses": "1.\tSome statistical figures, such as the radar chart summarizing multiple metrics, lack sufficient description. It is unclear what normalization or metrics were used for each axis.\n2.\tHow effectively do the Fisher Ratio and PH2 verify the discrimination of Hamming spaces? It is necessary to add more experimental analysis in Section 4.\n3.\tIt would be beneficial to test whether the global propagation remains stable under noisy or partially corrupted modalities—for instance, when the embedding graphs contain random noise—to verify robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ifWh0bRsDl", "reviewer": "Reviewer_CaBj", "summary": "This paper proposes DGHDGH, a new framework introducing hard negative generation into cross-modal hashing retrieval. The key idea is to model global semantic correlations among heterogeneous samples via a Relevance Global Propagation graph transformer, and synthesize channel-wise adaptive hard negatives using the Discriminative Global-sense Synthesis module. The method avoids relying solely on local pairwise interpolation, thereby maintaining semantic consistency in Hamming space. Extensive experiments across MIRFLICKR-25K, NUS-WIDE, and MS-COCO show state-of-the-art results.", "questions": "1. Are the loss weights fixed throughout training or tuned per dataset? Could the authors report whether the optimization of three loss terms exhibits any instability during early training stages?\n\n2. How are the radar-plot axes normalized, by relative gain or absolute metric value?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The RGP–DGS pipeline is an elegant architectural contribution combining graph-based correlation learning with adaptive synthesis.\n\n2. Provides a principled treatment of difficulty adaptation, moving beyond heuristic sampling.\n\n3. The experiments are extensive, statistically robust, and demonstrate consistent gains over diverse baselines.\n\n4. The approach is efficient and generalizable to existing hashing frameworks.", "weaknesses": "1. The three loss components (L_sp, L_is, L_cd) are optimized in parallel, yet the paper does not clarify their relative weights or potential gradient interactions. A short sensitivity analysis would strengthen the presentation.\n\n2. The experiments mainly use CLIP-ViT backbones; limited tests with other vision–language models (e.g., BLIP, SigLIP, ALIGN) make it difficult to judge generalization across architectures.\n\n3. The radar plot visualizing parameter sensitivity (Fig. 7) is not clearly described — axis meaning, normalization range, and metric selection should be elaborated to help readers interpret the results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Gf3XwuuG5S", "reviewer": "Reviewer_M2dA", "summary": "DGHDGH presents a technically elegant yet computationally efficient solution for enhancing cross-modal hashing. The paper integrates a lightweight graph propagation (RGP) and an adaptive interpolation module (DGS) into a CLIP-based framework, showing measurable improvements with minimal resource overhead. From an implementation viewpoint, the proposed pipeline is easy to reproduce and could serve as a plug-and-play enhancement to existing retrieval systems.", "questions": "1.Is λ initialized randomly or via prior heuristics?\n2.Could the authors quantify the overhead (Params / FLOPs) of RGP relative to a standard self-attention layer?\n3.For deployment, have the authors explored quantizing RGP parameters to further reduce inference cost? Some discussion on the theme matched with hashing retrieval would be welcome.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The RGP–DGS pipeline is an elegant architectural contribution combining graph-based correlation learning with adaptive synthesis.\n2.Provides a principled treatment of difficulty adaptation, moving beyond heuristic sampling.\n3.The experiments are extensive, statistically robust, and demonstrate consistent gains over diverse baselines.\n4.The approach is efficient (no extra generator) and generalizable to existing hashing frameworks.", "weaknesses": "1.The paper introduces λ as a channel-wise coefficient but does not explicitly state whether it is a fixed hyperparameter or a learned variable. Clarifying whether λ is shared between modalities would aid implementation.\n2.While the paper claims not to use additional generators, RGP is still a GNN-based component. It would be useful to show FLOPs or parameter comparisons between DGHDGH and baselines to substantiate the claim of efficiency.\n3.The RGP module resembles self-attention. Could the authors comment on whether it could be replaced by a lightweight transformer encoder?\n4.The paper alternates between the terms “Global-sense” and “Global correlation”, which may confuse readers. Unifying terminology at the beginning of Section 3 would make the exposition cleaner.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QbXKQAOB60", "reviewer": "Reviewer_Xgam", "summary": "This paper addresses the challenge of discriminative cross-modal hashing through a global-sense perspective, constructing adaptive hard negatives that reflect global semantics rather than local feature proximity. The approach builds upon two main components: a graph propagation network (RGP) for capturing higher-order semantic dependencies and a discriminative synthesis unit (DGS) that regulates interpolation difficulty via channel-wise weighting. Conceptually, this work reframes hard-negative generation as an optimization over a semantic manifold.", "questions": "1. Could the authors mathematically relate the RGP operation to spectral diffusion or Laplacian smoothing?\n2. How sensitive is the overall model to λ initialization?\n3. Discussing the future work like audio/video retrieval in Conclusion section would strengthen the impact.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Introduces a coherent, theoretically inspired motivation for rethinking negative sampling as a global consistency problem.\n2. The loss design reflects an interesting interplay between semantic preservation, interpolation similarity, and coefficient diversity.\n3. Empirical results validate the conceptual claims with strong mAP improvements.", "weaknesses": "1. In Figure 3, the comparison between DGHDGH and DHaPH shows large performance gains, but it is unclear whether both models use identical backbones and training setups. A controlled experiment would be necessary to ensure fairness.\n2. The RGP module remains largely intuitive, while its empirical benefits are evident, there is no theoretical analysis of how the propagation maintains information fidelity or prevents over-smoothing.\n3. The DGS module’s channel-wise λ weights perform well, but their dynamics are not visualized. A λ-distribution plot or feature-space interpolation visualization would clarify how “difficulty” is being modulated.\n4. The method is validated only for image-text retrieval. Can DDGSH extend to audio/video modalities?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)