Cross-model retrieval with deep learning for business application
Bibliographic record
Abstract
Abstract Cross-modal retravel has been used in many fields, such as business and search engines. Most search engines for business are text-based, but text-based search engines are limited by equipment and the strict requirement for knowledge. Text-based search needs keyboards to finish the search process, which requires users to have the knowledge of using keyboards. Compared to the text-based search, audio-based search has advantages. First, it avoids the traditional ways of inputting information. And it gets rid of the gap in time between inputting information for searching and getting useful information. In this paper, we propose a way to use audio to search images for business applications. We use deep learning to implement cross-modal retrieval systems between images and audio. We first extract features from images and audio respectively. And then we implement a neural network with two identical networks to learn the correspondence between images and audio. The first network extracts the features from images and audio further for calculation, and the second network learns whether two features from different modalities are related. This research provides a new way for business applications to search for information more instantly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".