Deep Optical Flow Supervised Learning With Prior Assumptions
Bibliographic record
Abstract
Traditional methods for estimating optical flow use variational model that includes data term and smoothness term, which can build a constraint relationship between two adjacent images and optical flow. However, most of them are too slow to be used in real-time applications. Recently, convolutional neural networks have been used in optical flow area successfully. Many current learning methods use large data sets that contain ground truth for network training, which can make use of prior knowledge to estimate optical flow directly. However, these methods overemphasize the factor of deep learning and ignore advantages of many traditional assumptions used in variational framework for optical flow estimation. In this paper, inspired by classical energy-based optical flow methods, we propose a novel approach for dense motion estimation, which combines traditional prior assumptions with supervised learning network. During training, the variation in image brightness, gradient and spatial smoothness are embedded in network. Our method is tested on both synthetic and real scenes. The experimental results show that employing the prior assumptions during training can obtain more detailed and smoothed flow fields and can improve the accuracy of optical flow estimation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".