Decoupling Spatial Pattern and its Movement Via Complex Factorization Over Orthogonal Filter Pairs
Bibliographic record
Abstract
Variations between related images (e.g. due to motions) can caused by different independent factors. A qualified representation can decouple the underlying explanatory factors rather than keeping them mixed. After decoupling, each factor lies in a lower dimension abstract space. Different computer vision tasks can be done in different abstract spaces more efficiently than in the original pixel space. For example, conducting object recognition in appearance space can result in an invariant recognition; estimating object motion in location space yields a result regardless of the object itself. In this paper, we propose an algorithm to decouple object appearance and location to amplitude and phase in static images by using complex factorization over orthogonal filter pairs. In particular, we show that, i) Orthogonal filter pairs can be learned in an unsupervised manner from multiple consecutive frames; ii) Object movement is encoded in the factorized phase gradient between frames over time. As a proof of concept, we present experiments on the application of our framework to the recovery of the optical flow. Here object movement is successfully captured by phase gradient.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".