A Study on the Application of Housing Elements in Urban Cultural Heritage in Cultural Creation and Animation Scene Designs
Bibliographic record
Abstract
The inheritance and protection of urban cultural heritage faces the dilemma of narrow coverage and lack of change in form, and to solve this dilemma, we need to find a breakthrough in cultural creation and animation design, and carry out creative activities and popularisation among all people.The article proposes a feature extraction model that integrates multi-scale features and housing element information mining, and applies it to the feature extraction of housing elements in urban cultural heritage.A hybrid attention module is embedded in the ResNet-18 backbone network to enhance housing element features and suppress redundant information, and a CEB module and learnable parameters are combined to filter out the background information of the low-level features, so as to obtain finer architectural housing element features.The extracted housing elements are used as the basis for the design of creative products and animation scenes, and the feasibility of the programme is investigated through questionnaires.The overall evaluation mean value of the research respondents on the design of cultural and creative products for the housing was 7.64 points, and more than 95% of the evaluation respondents indicated that the housing elements were more suitable for the animation scene design.Relying on modern technology to extract housing elements from urban cultural heritage and realising the innovative application of cultural heritage in the form of cultural creation and animation provides a new path for the revitalisation and inheritance of urban cultural heritage.
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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 | 0.001 | 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 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".