Multi‐feature consultation model for human action recognition in depth video sequence
Why this work is in the frame
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Bibliographic record
Abstract
In the field of computer vision research, the research on human action recognition of depth video sequence is an important research direction. Herein, considering the characteristics of temporal and spatial depth video sequence, the authors propose a framework of the consultation model of several action sequence features to solve the classification problem in‐depth video sequence. According to the characteristics of the 3D human action space, a variety of action sequence feature data is obtained, and then these data is projected to three coordinate planes, the acquired fusion features are used to train the consultation model, and finally the model is validated through the data. The authors have achieved good results by comparing the two publicly available datasets with the other methods. Experimental results demonstrate that the model performs well in existing identification methods.
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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 | 0.001 |
| 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 it