Deep learning models for lightness constancy can exploit both natural lighting cues and rendering artifacts.
Bibliographic record
Abstract
We previously showed that deep learning models that estimate intrinsic image components (albedo and illuminance) outperform classic models on lightness constancy tasks. Here, we examine what cues these models rely on. We considered two cue types: natural features such as shadows and shading, and artifacts of ray tracing softwares, which typically produce a residual rendering noise that varies with local illumination. We rendered training, validation and test sets via (1) ray tracing (Blender/Cycles) with 128 photons sampled per pixel (high residual noise); (2) same as (1) but 1024 photons sampled (low noise); (3) Blender’s EEVEE renderer (rasterization engine, no noise). (Noise artifacts are also found in other ray tracing renderers, including Mitsuba.) Networks trained on EEVEE images showed similar performance on all three test sets (and performed much better than classic models), whereas networks trained on Cycles showed best performance with Cycles test images, and worst performance with EEVEE images. To assess dependence on naturalistic cues, we tested the networks on test images with various scene elements removed: (1) cast shadows on the floor; (2) shading; (3) all shadows and shading. In (3), no naturalistic lighting cues were available, and yet models trained on Cycles keep a partial, if low, constancy. These models were also almost unaffected by the removal of shadows and shading (less than 10% decrease in constancy). However, the model trained on EEVEE showed a 50% decrease in constancy when floor shadows were removed, and had lowest constancy in condition (3). These results show that widely used ray tracing methods typically produce artifacts that networks can exploit to achieve lightness constancy. When these artifacts are avoided, networks rely on more naturalistic lighting cues, and still exhibit human levels of constancy. Thus deep networks provide a promising starting point for image-computable models of human lightness and color perception.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".