The comparison of top-down and bottom-up methods in multi-person pose estimation
Why this work is in the frame
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Bibliographic record
Abstract
2D multi-person pose estimation is a process to detect all bodies in a two-dimensional picture. The main purpose of this report is to discuss the difference between top-down and bottom- up method in multi-person human pose estimation. This estimation will focus on many people in one picture. There are two popular methods in this area. The first is top-down method, which is to find people firstly and then to detect the body of each people. On the other hand, the bottom-up method is to detect body parts and then find each person. Their comparison and analyse in algorithm, speed and accuracy may help researchers to find more suitable methods when they research in human pose estimation. Generally, top-down methods have higher accuracy than bottom-up methods but have higher speed.
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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 | 0.000 |
| 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 it