Improved Cross-Modal Retrieval Systems Using Self-Reinforcement and Quadruplet Alignment Hashing
Bibliographic record
Abstract
Cross-modal retrieval presents significant challenges for consumer technology applications, demanding innovative approaches to bridge semantic gaps between different data modalities while ensuring efficient information access. This paper introduces a novel Self-Reinforcement and Quadruplet Alignment Hashing (SRQA) framework specifically designed to enhance cross-modal retrieval systems for improved user experiences. Our approach distinguishes itself through three key contributions. First, we develop a dynamic unified similarity matrix that adaptively balances label-driven semantic information with modality-specific correlations, enabling more nuanced cross-modal representations than traditional fixed alignment strategies. Second, we propose a novel quadruplet-based hashing method that implements an efficient hard sample mining strategy through the refinement of both absolute and relative distance constraints between samples, thereby providing a more precise and efficient semantic alignment mechanism for cross-modal retrieval. Third, through extensive experiments conducted on three benchmark datasets—MIRFLICKR-25K, NUS-WIDE, and MS-COCO—our framework consistently outperforms ten state-of-the-art cross-modal retrieval methods across various hash code lengths, offering significant advancements for consumer technology applications requiring efficient multi-modal information retrieval.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".