DisCrossFormer: A Deep Light-Weight Image Super Resolution Network Using Disentangled Visual Signal Processing and Correlated Cross-Attention Transformer Operation
Why this work is in the frame
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Bibliographic record
Abstract
Deep neural networks that employ transformer operations have provided state-of-the-art performances for the task of image super resolution (SR). However, processing the non-local information in the visual signals by the transformers often involves increasing the network complexity. In order to develop a light-weight SR network that can process non-local information for providing superior performance, in this paper, we propose the correlated cross-attention operation. Further, we design a novel overall architecture for our SR network, which processes the disentangled information of the low-resolution images based on the presence of various objects in the visual signals. Disentangling the information of the input low-resolution images facilitates learning by paying more attention to processing a certain number of objects (and not all of them) in the visual signals at a time. The results of various experiments show the effectiveness of both ideas in generating super-resolved images with higher qualities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.008 |
| 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