Bibliographic record
Abstract
Superpixels are becoming increasingly popular with the advent of new semantic segmentation and artificial vision techniques. As these techniques improve, there is a need for superpixel methods that can capture finer details. In this paper, we propose a new superpixel method called Robust Adaptive Image Clustering (RAIC), which, in addition to having the capability of capturing fine details, also offers the following advantages compared to other methods: high quality clustering, adaptive seed placement, compactness, robustness to noise, computational efficiency, and low algorithmic complexity. In addition, our algorithm has the tremendous advantage of being able to be written in the form of a cellular automaton. Cell automata are easy to parallelize, even massively, allowing for large performance gains when implemented for multithreaded CPU or GPU environments. Experimental results show that RAIC outperforms state-of-the-art superpixel segmentation algorithms. Furthermore, the results of the quantitative evaluation confirm the validity of the qualitative visual comparison of the superpixel image reconstructions.
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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 | 0.000 | 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 | 0.000 | 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 | 0.001 |
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 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".