Enhancing Intrinsic Image Decomposition with Transformer and Laplacian Pyramid Network
Bibliographic record
Abstract
Intrinsic Image Decomposition (IID) remains a pivotal challenge in the domain of computer vision, with applications spanning image editing, color image denoising, and segmentation, among others.Despite notable successes, there exists a significant opportunity for enhancing the feature encoding process to improve the accuracy of predicted outcomes.In response to this, a novel framework, termed Transformer and Laplacian Pyramid Network (TLPNet), is introduced.TLPNet comprises two distinct sub-networks: the Transformer for Reflectance Network (TRNet) and the Laplacian Pyramid for Shading Network (LPSNet).Within this framework, the Transformer module is strategically employed within the reflectance imaging component to effectively address the challenge of inadequate feature information extraction.Comprehensive experiments conducted on the ShapeNet Dataset and MIT Dataset have demonstrated the efficacy of TLPNet in predicting more accurate reflectance and shading images.This study contributes to the field by presenting an innovative approach that leverages the strengths of transformer models and Laplacian pyramid structures for the task of IID, setting a new benchmark for future research in the area.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".