Learning illumination- and orientation-invariant representations of objects throughtemporal association
Bibliographic record
Abstract
As the orientation or illumination of an object changes so does its appearance. This paper considers how observers are nonetheless able to recognize objects that have undergone such changes. In particular the paper tests the hypothesis that observers rely on temporal correlations between different object views to decide whether they are views of the same object or not. In a series of experiments subjects were shown a sequence of views representing a slowly transforming object. Testing revealed that subjects had formed object representations which were directly influenced by the temporal characteristics of the training views. In particular, introducing spurious correlations between views of different people's heads caused subjects to regard those views as being of a single person. This rapid and robust overriding of basic generalization processes supports the view that our recognition system tracks the correlated appearance of views of objects across time. Such view associations appear to allow the visual system to solve the view invariance problem without recourse to complex illumination models for extracting 3D form, or the use of the image plane transformations required to make appearance-based comparisons.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".