Refining attention weights for facial super-resolution with counterfactual attention learning
Why this work is in the frame
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Bibliographic record
Abstract
Face Super-resolution is a challenging problem involving reconstructing High- Resolution (HR) images from Low-Resolution (LR) inputs with attention mechanisms being a widely used approach. This paper introduces counterfactual attention learning (CAL), a novel framework based on causal inference that enhances attention quality in super-resolution tasks. CAL provides a strong supervisory signal, enabling the refinement of attention mechanisms during training. Through counterfactual interventions, CAL optimizes learned attention to improve super-resolution outcomes. This method is evaluated using the Scale- Arbitrary Super-Resolution model (ArbSR), which accommodates non-integer scale factors. Experiments conducted on CelebA, FFHQ, and CMU Multi-PIE datasets across different scale factors show that CAL significantly enhances super-resolution performance. On the CMU Multi-PIE dataset, CAL improves Peak Signal-to-Noise Ratio (PSNR) by up to 13.6 % compared to baseline attention mechanisms, even under challenging variations in illumination, pose, and expression. PSNR improvement of 15.5 % was observed for CelebA dataset whereas for the FFHQ dataset, 14.5 % improvement was observed under occlusion conditions. These results highlight the robustness and effectiveness of CAL in advancing the state of super-resolution, offering substantial quantitative and qualitative improvements and showcasing its potential for face superresolution in real-world conditions.
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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 | 0.000 |
| 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 it