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Deep Global-sense Hard-negative Discriminative Generation Hashing for Cross-modal Retrieval

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Deep HashingCross-modal RetrievalInformative LearningHard Negative Generation
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.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes DGHDGH, a framework that synthesizes hard negatives by capturing global embedding geometry through structured graph propagation and difficulty-adaptive interpolation. It occupies the 'Global-Sense Hard Negative Generation' leaf within the 'Hard Negative Mining and Discriminative Learning' branch. Notably, this leaf contains only the original paper itself—no sibling papers were identified in the taxonomy. This suggests the global-sense perspective on hard negative generation represents a relatively sparse or emerging research direction within cross-modal hashing, contrasting with more populated areas like contrastive learning or triplet-based methods.

The taxonomy reveals that neighboring leaves include 'Adaptive Triplet-Based Hard Negative Learning' (one paper), 'Contrastive Learning with Negative Sampling' (two papers), and 'Noise-Robust Negative Mining' (one paper). These directions emphasize local pairwise constraints, momentum-based memory banks, or noise handling, whereas the original paper's global graph propagation approach diverges by modeling dataset-wide correlations. The 'Semantic Preservation and Cross-Modal Alignment' branch (four papers) focuses on feature interaction and label correlation without explicit hard negative mechanisms, further highlighting the distinctiveness of the global-sense synthesis strategy within the hard negative mining paradigm.

Among 21 candidates examined, the DGS module (Contribution 3) encountered one refutable candidate, while the DGHDGH framework (Contribution 1, 10 candidates) and RGP module (Contribution 2, 10 candidates) showed no clear refutations. The limited search scope means these statistics reflect top-K semantic matches and citation expansion, not exhaustive coverage. The DGS module's overlap suggests that difficulty-adaptive interpolation may have partial precedent, whereas the global propagation mechanism and overall framework appear less directly anticipated by the examined prior work. The sparse taxonomy leaf and low refutation rate across most contributions indicate the approach occupies a relatively novel niche.

Based on the limited search of 21 candidates and the taxonomy structure, the work appears to introduce a distinctive global-sense perspective on hard negative generation, diverging from local correlation methods prevalent in neighboring leaves. The absence of sibling papers and minimal refutations suggest novelty, though the analysis does not cover the full literature landscape. The DGS module's partial overlap warrants closer scrutiny, but the overall framework's integration of global graph propagation with Hamming-space synthesis seems less directly addressed by the examined prior work.

Taxonomy

Core-task Taxonomy Papers
13
3
Claimed Contributions
21
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: cross-modal hashing for retrieval with hard negative generation. The field addresses efficient similarity search across modalities (e.g., image and text) by learning compact binary codes while emphasizing discriminative power through hard negative mining. The taxonomy reveals several main branches: Hard Negative Mining and Discriminative Learning focuses on selecting challenging samples to sharpen decision boundaries, often using contrastive or triplet-based strategies (e.g., Momentum Contrastive Hashing[5], Gradient-Triplet Hashing[8]); Semantic Preservation and Cross-Modal Alignment emphasizes maintaining label correlations and semantic consistency (e.g., Label Correlation Hashing[6], Semantic Graph Embedding[10]); Generative and Adversarial Approaches leverage GANs to synthesize hard negatives or refine hash codes (e.g., SCH-GAN[11], Unsupervised GAN Hashing[12]); Adversarial Robustness and Attack Methods explore vulnerabilities and defenses in hashing systems (e.g., BACH Attack[9], Cross-gen Attack[3]); and Cross-Modal Indexing and Retrieval Optimization tackles structural properties of Hamming space and domain-specific retrieval challenges (e.g., Hamming Space Properties[1], Remote Sensing Ship Retrieval[2]). A particularly active line of work centers on contrastive and momentum-based methods that dynamically mine hard negatives during training, balancing discriminative power with computational efficiency. Another contrasting direction uses generative models to explicitly synthesize challenging samples, though this can introduce additional training complexity. Global Hard-negative Hashing[0] sits within the Hard Negative Mining and Discriminative Learning branch, specifically under Global-Sense Hard Negative Generation, where it emphasizes a holistic view of negative selection rather than local pairwise comparisons. Compared to Momentum Contrastive Hashing[5], which relies on a memory bank for dynamic negatives, and Contrastive Discrete Hashing[4], which focuses on discrete optimization, Global Hard-negative Hashing[0] appears to prioritize a global perspective on hard negative sampling, potentially offering more comprehensive discriminative signals across the entire dataset.

Claimed Contributions

DGHDGH framework for cross-modal hashing with global-sense hard negative generation

The authors introduce DGHDGH, a novel framework that is the first to incorporate hard negative generation into cross-modal hashing. It uses graph-based global correlation modeling and adaptive interpolation to produce informative negatives that enhance discriminative retrieval in Hamming space.

10 retrieved papers
Relevance Global Propagation (RGP) module

The RGP module employs graph neural networks with dual-iterative message propagation to learn global sample correlations across the entire batch, enabling the model to determine appropriate difficulty levels for synthetic negatives while preserving semantic consistency.

10 retrieved papers
Discriminative Global-sense Synthesis (DGS) module

The DGS module performs channel-wise adaptive interpolation guided by global correlations learned from RGP, generating hard negatives with difficulty levels that adapt per channel and evolve during training, without requiring an extra generator network.

1 retrieved paper
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DGHDGH framework for cross-modal hashing with global-sense hard negative generation

The authors introduce DGHDGH, a novel framework that is the first to incorporate hard negative generation into cross-modal hashing. It uses graph-based global correlation modeling and adaptive interpolation to produce informative negatives that enhance discriminative retrieval in Hamming space.

Contribution

Relevance Global Propagation (RGP) module

The RGP module employs graph neural networks with dual-iterative message propagation to learn global sample correlations across the entire batch, enabling the model to determine appropriate difficulty levels for synthetic negatives while preserving semantic consistency.

Contribution

Discriminative Global-sense Synthesis (DGS) module

The DGS module performs channel-wise adaptive interpolation guided by global correlations learned from RGP, generating hard negatives with difficulty levels that adapt per channel and evolve during training, without requiring an extra generator network.

Table of Contents