IDTransformer: Transformer for Intrinsic Image Decomposition
Bibliographic record
Abstract
The aim of intrinsic image decomposition (IID) is to recover reflectance and the shading from a given image. As different combinations are possible, IID is an under constrained problem. Previous approaches try to constrain the search space using hand crafted priors. However, these priors are based on strong imaging assumptions and fall short when these do not hold. Deep learning based methods learn the problem end-to-end from the data. But these networks lack any explicit information about the image formation model.In this paper, an IID transformer approach (IDTransformer) is proposed by learning photometric invariant attention, derived from the image formation model, integrated in the transformer framework. The combination of invariant features in both a global and local setting allows the network to not only learn reflectance transitions, but also to group similar reflectance regions, irrespective of the spatial arrangement. Illumination and geometry invariant attention is exploited to generate the reflectance map, while illumination invariant and geometry variant attention is used to compute the shading map.Enabling physics-based explicit attention allows the network to be trained on a relatively small dataset. Ablation studies show that adding invariant attention improves the performance. Experiments on the Intrinsic In the Wild dataset shows competitive results with competing methods. The project page with the code is available at https://morpheus3000.github.io/IDTransformer.web/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".