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Hierarchical Encoding Tree with Modality Mixup for Cross-modal Hashing

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Cross-modal HashingUnsupervised Hash RetrievalCross-modal Retireval
Abstract:

Cross-modal retrieval is a significant task that aims to learn the semantic correspondence between visual and textual modalities. Unsupervised hashing methods can efficiently manage large-scale data and can be effectively applied to cross-modal retrieval studies. However, existing methods typically fail to fully exploit the hierarchical structure between text and image data. Moreover, the commonly used direct modal alignment cannot effectively bridge the semantic gap between these two modalities. To address these issues, we introduce a novel Hierarchical Encoding Tree with Modality Mixup (HINT) method, which achieves effective cross-modal retrieval by extracting hierarchical cross-modal relations. HINT constructs a cross-modal encoding tree guided by hierarchical structural entropy and generates proxy samples of text and image modalities for each instance from the encoding tree. Through the curriculum-based mixup of proxy samples, HINT achieves progressive modal alignment and effective cross-modal retrieval. Furthermore, we conduct cross-modal consistency learning to achieve global-view semantic alignment between text and image representations. Extensive experiments on a range of cross-modal retrieval datasets demonstrate the superiority of HINT over state-of-the-art methods.

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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 a hierarchical encoding tree guided by structural entropy to extract multi-granularity cross-modal relations, combined with curriculum-based modality mixup and global consistency learning. It resides in the 'Multi-Similarity and Multi-Granularity Reconstruction' leaf under 'Similarity Preservation and Reconstruction Strategies', which contains five papers total. This leaf represents a moderately populated research direction within the broader taxonomy of fifty papers, indicating active but not overcrowded exploration of hierarchical and multi-level semantic structures in unsupervised cross-modal hashing.

The taxonomy reveals that neighboring leaves address related but distinct strategies: 'Semantic and Joint-Modal Reconstruction' (five papers) focuses on joint semantic alignment without explicit hierarchical encoding, while 'Graph-Based Similarity Preservation' (four papers) leverages neighbor relationships rather than tree structures. The 'Contrastive and Adversarial Learning Frameworks' branch (seven papers) pursues discriminative objectives instead of reconstruction-based similarity preservation. The scope note for the paper's leaf explicitly excludes single-similarity or single-level methods, positioning this work among approaches that reconstruct multiple types of similarity or multi-level semantic structures, distinguishing it from simpler reconstruction strategies in sibling categories.

Among thirty candidates examined, the hierarchical encoding tree contribution shows two refutable candidates out of ten examined, suggesting some prior work on hierarchical or tree-based structures exists within the limited search scope. The curriculum-based modality mixup and proxy-based global consistency learning contributions each examined ten candidates with zero refutable matches, indicating these mechanisms appear more distinctive within the analyzed literature. The analysis does not claim exhaustive coverage; rather, it reflects patterns observed in top-K semantic matches and citation expansion, leaving open the possibility of additional relevant work beyond this scope.

Given the limited search scale and the moderately populated taxonomy leaf, the work appears to combine established ideas (hierarchical structures, similarity reconstruction) with novel mechanisms (curriculum mixup, proxy-based consistency) in a way that differentiates it from immediate neighbors. The two refutable candidates for the encoding tree suggest incremental refinement rather than radical departure, while the mixup and consistency components show no clear overlap in the examined set. A broader literature search might reveal additional connections, but within the analyzed scope, the integration of these elements offers a recognizable contribution to multi-granularity cross-modal hashing.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: cross-modal retrieval using unsupervised hashing methods. The field organizes around several complementary strategies for learning compact binary codes that bridge different modalities without labeled supervision. Similarity Preservation and Reconstruction Strategies focus on maintaining pairwise or multi-granularity relationships while reconstructing original features, as seen in works like Hierarchical Consensus Hashing for[2] and Multi-similarity reconstructing and clustering-based[20]. Contrastive and Adversarial Learning Frameworks leverage discriminative objectives to align modalities, exemplified by Unsupervised Contrastive Cross-Modal Hashing[8] and Deep Unsupervised Momentum Contrastive[6]. Modality Interaction and Fusion Mechanisms emphasize how to effectively combine heterogeneous data streams through graph convolution or attention, while Pre-Trained Model and Knowledge Transfer Approaches exploit large-scale models like CLIP to inject semantic priors into hashing, as in CLIP4Hashing[12]. Specialized Optimization and Efficiency Strategies address scalability and convergence challenges, and Domain-Specific and Application-Oriented Methods tailor solutions to particular retrieval scenarios. Recent work increasingly explores multi-granularity and hierarchical structures to capture richer semantic correspondences. Hierarchical Encoding Tree with[0] sits within the Similarity Preservation and Reconstruction branch, emphasizing multi-similarity and multi-granularity reconstruction alongside neighbors like Multi-Grained Similarity Preserving and[49] and Cross-media Hash Retrieval Using[50]. Compared to High-order nonlocal hashing for[3], which focuses on nonlocal graph relationships, Hierarchical Encoding Tree with[0] adopts a tree-based encoding strategy to preserve hierarchical semantic structures across modalities. Meanwhile, Revising similarity relationship hashing[5] revisits fundamental similarity metrics, offering a contrasting perspective on how to define and preserve cross-modal affinities. These diverse approaches reflect ongoing debates about whether to prioritize local versus global similarity, single-level versus hierarchical representations, and reconstruction fidelity versus discriminative power in unsupervised cross-modal hashing.

Claimed Contributions

Hierarchical encoding tree for cross-modal hashing

The authors propose constructing a cross-modal encoding tree guided by hierarchical structural entropy to recover hierarchical semantic structures and uncover local semantic communities, addressing the limitation of flat and sparse cross-modal connections in unsupervised scenarios.

10 retrieved papers
Can Refute
Curriculum-based modality mixup mechanism

The method generates proxy samples from different modalities for each instance using the encoding tree, then performs curriculum-based mixup to achieve progressive alignment between heterogeneous modalities, avoiding the difficulty of direct cross-modal alignment.

10 retrieved papers
Proxy-based global consistency learning

The authors introduce a consistency learning mechanism that optimizes the distribution alignment of proxy samples across modalities to achieve global-level semantic alignment, complementing the local hierarchical modeling.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Hierarchical encoding tree for cross-modal hashing

The authors propose constructing a cross-modal encoding tree guided by hierarchical structural entropy to recover hierarchical semantic structures and uncover local semantic communities, addressing the limitation of flat and sparse cross-modal connections in unsupervised scenarios.

Contribution

Curriculum-based modality mixup mechanism

The method generates proxy samples from different modalities for each instance using the encoding tree, then performs curriculum-based mixup to achieve progressive alignment between heterogeneous modalities, avoiding the difficulty of direct cross-modal alignment.

Contribution

Proxy-based global consistency learning

The authors introduce a consistency learning mechanism that optimizes the distribution alignment of proxy samples across modalities to achieve global-level semantic alignment, complementing the local hierarchical modeling.

Table of Contents