Exploration of computer vision-based non-heritage cultural pattern recognition and reconstruction methods in cultural and creative design
Bibliographic record
Abstract
In order to optimize the design effect of cultural and creative products with non-heritage patterns, this paper uses image reconstruction algorithm and image recognition algorithm to process non-heritage problem patterns.By combining the processed non-heritage cultural patterns with consumer demand for cultural and creative products, non-heritage cultural pattern cultural and creative products are designed to meet market demand.On the basis of recursive network, we add multi-scale feature extraction module and attention feature fusion module, choose 1 L loss function to optimize the details of image reconstruction, and construct image super-resolution reconstruction algorithm based on multi-scale recursive attention feature fusion network.And the image feature extraction network containing MSA module is designed, which is the fine-grained image recognition network based on multi-scale attention.The non-heritage cultural pattern dataset is established, and in order to optimize the recognition rate of non-heritage patterns, the image reconstruction based on multi-scale recursive attention feature fusion network is carried out on the non-heritage cultural pattern data.In view of the creative design strategy of non-heritage culture, the evaluation indexes of non-heritage cultural and creative product design are obtained from the consumer research, and the implementation suggestions of non-heritage pattern cultural and creative product design are derived based on the ranking of the importance of the evaluation indexes.The multi-scale recursive attention feature fusion network proposed in this paper achieves 34.89dB and 90.52% indicator scores on the Set5 dataset.For the design of cultural and creative products with non-heritage patterns, consumers make more suggestions in terms of functional differentiation, having a response rate of 21.58%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".