Cross-Media Retrieval Based on Two-Level Similarity and Collaborative Representation
Bibliographic record
Abstract
In the exploration of cross-media retrieval encompassing images and text, an advanced method incorporating two-level similarity and collaborative representation (TLSCR) is presented.Initially, two sub-networks were designed to handle both global and local features, facilitating enhanced semantic associations between images and textual content.Whole images, along with regional image sectors, served as representations for images, while textual content was depicted both through complete sentences and select keywords.An innovative two-level alignment approach was introduced to segregate and then amalgamate the global and local depictions of paired images and texts.Subsequently, employing collaborative representation (CR) technology, each experimental image was collaboratively reconstructed by utilising the entirety of the training images, and every experimental text by incorporating all the training texts.The collaborative coefficients derived were subsequently employed as congruent dimensional representations for both images and texts.Upon completion of these operations, cross-media retrieval between the two modalities was conducted.Experimental outcomes on datasets like Wikipedia and Pascal Sentence confirm the superior precision of the proposed method, surpassing conventional cross-media retrieval methodologies.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".