Improved Cross-Modal Retrieval Systems Using Self-Reinforcement and Quadruplet Alignment Hashing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it