A Flexible Content-Adaptive Mesh-Generation Strategy for Image Representation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Based on the greedy-point removal (GPR) scheme of Demaret and Iske, a simple yet highly effective framework for constructing triangle-mesh representations of images, called GPRFS, is proposed. By using this framework and ideas from the error diffusion (ED) scheme (for mesh-generation) of Yang et al., a highly effective mesh-generation method, called GPRFS-ED, is derived and presented. Since the ED scheme plays a crucial role in our work, factors affecting the performance of this scheme are also studied in detail. Through experimental results, our GPRFS-ED method is shown to be capable of generating meshes of quality comparable to, and in many cases better than, the state-of-the-art GPR scheme, while requiring substantially less computation and memory. Furthermore, with our GPRFS-ED method, one can easily trade off between mesh quality and computational/memory complexity. A reduced-complexity version of the GPRFS-ED method (called GPRFS-MED) is also introduced to further demonstrate the computational/memory-complexity scalability of our GPRFS-ED method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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