Dual-stream multi-layer cross encoding network for texture analysis of architectural heritage elements
Bibliographic record
Abstract
Texture provides valuable insights into building materials, structure, style, and historical context. However, traditional deep learning features struggle to address architectural textures due to complex inter-class similarities and intra-class variations. To overcome these challenges, this paper proposes a Dual-stream Multi-layer Cross Encoding Network (DMCE-Net). DMCE-Net treats deep feature maps from different layers as experts, each focusing on specific texture attributes. It includes two complementary encoding streams: the intra-layer encoding stream efficiently captures diverse texture perspectives from individual layers through multi-attribute joint encoding, while the inter-layer encoding stream facilitates mutual interaction and knowledge integration across layers using a cross-layer binary encoding mechanism. By leveraging collaborative interactions between both streams, DMCE-Net effectively models and represents complex texture attributes of architectural heritage elements. Extensive experimental evaluations on architectural heritage datasets and three texture databases demonstrate that DMCE-Net achieves superior performance compared to existing deep learning methods and handcrafted features, providing reliable texture representations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".