BigEPIT: Scaling EPIT for Light Field Image Super-Resolution
Bibliographic record
Abstract
Existing methods have been developed for light field (LF) image Super-Resolution (SR) and achieved continuously improved performance while suffering a significant performance drop when handling scenes with large disparity variations. EPIT [1] was proposed to mitigate the disparity issue through non-local spatial-angular correlation learning. However EPIT has limitations due to the limited scale of existing LF datasets and the presence of imbalanced LF disparity, especially the scarcity of large disparity. To address this issue, we present a series of strategies to scale EPIT, called BigEPIT, including compound model scaling, augmented data resampling, and a high-precision test scheme. Specifically, the compound scaling method simultaneously scales the depth and width of the model to better improve the model capability. The augmented resampling method employs varying sampling intervals during training data generation, rather than solely relying on the central region view. This approach mitigates issues related to disparity imbalance and overfitting. The patch-based test scheme is popular because of its small GPU memory footprint. The traditional zero padding method and window partition will destroy the LF disparity structure and degrade the performance. Moreover, we find a positive correlation between the performance and the patchsize. Therefore, we advocate a high-precision test scheme i.e., a full-size or larger patchsize without zero padding for testing wherever the GPU memory permits, to achieve superior results. Extensive experiments demonstrate the effectiveness of our proposed method, which ranked 1st place in the NTIRE 2024 Light Field Image Super-Resolution Challenge.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".