Replacing Averaging with More Powerful Self-Attention Mechanism for Multi-Image Super-Resolution
Why this work is in the frame
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Bibliographic record
Abstract
In the field of multi-image super-resolution, most advanced models adopt a strategy of calculating an increment and then adding it to a baseline image. However, most existing work focuses on obtaining the increment by modeling the correlation between input images using deep learning techniques, while little attention is paid to the computation of the baseline, which is typically obtained by simply averaging the input images. This paper proposes an improved model that replaces averaging with self-attention mechanism in the existing PIUNet model, which makes the baseline computation phase more powerful. The experimental results show that compared to the original model, our improved model not only shows improvements over the state of the art models on a subset of the PROBA-V dataset but also reduces the required training time.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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