A characterization of visual feature recognition
Bibliographic record
Abstract
Natural human interfaces are a key to realizing the dream of ubiquitous computing. This implies that embedded systems must be capable of sophisticated perception tasks. This paper analyzes the nature of a visual feature recognition workload. Visual feature recognition is a key component of a number of important applications, e.g. gesture based interfaces, lip tracking to augment speech recognition, smart cameras, automated surveillance systems, robotic vision, etc. Given the power sensitive nature of the embedded space and the natural conflict between low-power and high-performance implementations, a precise understanding of these algorithms is an important step in developing efficient visual feature recognition applications for the embedded space. In particular, this work analyzes the performance characteristics of flesh toning, face detection and face recognition codes based on well known algorithms. We show that the problem can be decomposed into a pipeline of filters which could lead to efficient implementations as stream processors. With better than 92% hit rate for a modest 16KB L1 data cache, the algorithms have memory system behavior commensurate with embedded processors. However, our results indicate that their execution requirements strain the performance available on current embedded systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".