A Stream Algebra for Performance Optimization of Large Scale Computer Vision Pipelines
Bibliographic record
Abstract
There is a large growth in hardware and software systems capable of producing vast amounts of image and video data. These systems are rich sources of continuous image and video streams. This motivates researchers to build scalable computer vision systems that utilize data-streaming concepts for processing of visual data streams. However, several challenges exist in building large-scale computer vision systems. For example, computer vision algorithms have different accuracy and speed profiles depending on the content, type, and speed of incoming data. Also, it is not clear how to adaptively tune these algorithms in large-scale systems. These challenges exist because we lack formal frameworks for building and optimizing large-scale visual processing. This paper presents formal methods and algorithms that aim to overcome these challenges and improve building and optimizing large-scale computer vision systems. We describe a formal algebra framework for the mathematical description of computer vision pipelines for processing image and video streams. The algebra naturally describes feedback control and provides a formal and abstract method for optimizing computer vision pipelines. We then show that a general optimizer can be used with the feedback-control mechanisms of our stream algebra to provide a common online parameter optimization method for computer vision pipelines.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".