Thanka Mural Inpainting Based on Multi-Scale Adaptive Partial Convolution and Stroke-Like Mask
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Bibliographic record
Abstract
Thanka murals are important cultural heritages of Tibet, but many precious murals were damaged during history. Thanka mural restoration is very important for the protection of Tibetan cultural heritage. Partial convolution has great potential for Thanka mural restoration due to its outstanding performance for inpainting irregular holes. However, three challenges prevent the existing partial convolution-based methods from solving Thanka restoration problems: 1) the features of multi-scale objects in Thanka murals cannot be extracted correctly because of single-scale partial convolution; 2) the stroke-like Thanka inpainting mode cannot be effectively simulated and learned by existing rectangular or arbitrary masks; and 3) the original content of damaged Thanka murals cannot be restored. To resolve these problems, we propose a Thanka mural inpainting method based on multi-scale adaptive partial convolution and stroke-like masks. The proposed method consists of three parts: 1) a kernel-level multi-scale adaptive partial convolution (MAPConv) to accurately discriminate valid pixels from invalid pixels, and to extract the features of multi-scale objects; 2) a parameter-configurable stroke-like mask generation method to simulate and learn the stroke-like Thanka inpainting mode; and 3) a 2-phase learning framework based on MAPConv Unet and different loss functions to restore the original content of Thanka murals. Experiments on both simulated and real damages of Thanka murals demonstrated that our approach works well on a small dataset (N=2780), generates realistic mural content, and restores the damaged Thanka murals with high speed (600 ms for multiple holes in 512×512 images). The proposed end-to-end method can be applied to other small datasets-based inpainting tasks.
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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 | 0.001 | 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