Research on Diversified Inheritance Paths under the Analysis of Artistic Characteristics and Popular Elements of Guanzhong Drum and Gong Dance Based on Image Processing Algorithm
Bibliographic record
Abstract
In recent years, with the rapid development of artificial intelligence, big data, machine learning and other technologies, human society is entering a more and more intelligent society, and the interaction between humans and machines becomes more and more common.In this paper, image processing operations are added on the basis of Kinect's original acquisition of gong dance images, which reduces the influence of external light, background and other factors, and makes the human capture efficiency increase dramatically, and a spatio-temporal graph is constructed on the basis of the continuous human posture key point data, which describes the distribution of the human posture key points in different dataset types.Aiming at the problems existing in the traditional spatio-temporal map convolutional network, a multi-dimensional attention mechanism is designed to guide the model to reasonably allocate the weight resources in three dimensions: space, time and channel, respectively.Experiments are conducted on NTU-RGB+D, Kinect skeleton and Taiji datasets, respectively, which show that the AGCN-STC proposed in this paper has better recognition performance on all three datasets, and the recognition accuracy is improved by 0.9 percentage points compared with AM-GCN.Two actors are used as samples for visual measurement and quantitative analysis to compare the differences between the performance gestures of the two ornaments.Finally, based on the results of the study, we propose a transmission path for the Guanzhong gong dance, which is a reference for the cultural transmission of the Guanzhong gong dance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".