Improving Segmentation Boundaries with Nonparametric Image Parsing
Bibliographic record
Abstract
Semantic segmentation, or segmenting all the objects in an image is one of the core problems of computer vision. In order to achieve an object-level semantic segmentation, we propose to label image regions and to improve the segmentation result based on these labels. We build upon the recent super parsing approach, which is a nonparametric solution to the image labelling problem. We propose to initialize the segmentation with SLICO super pixels because SLICO is able to produce accurate boundaries and offers control over size, shape and compactness of the super pixels. These super pixels are labelled with super parsing but an optimization step is required for the large number of small super pixels. We formulate a Conditional Random Field (CRF) using a novel pair wise cost depending on local features and computed in a nonparametric estimation. This results in stronger semantic contextual constraints. We evaluate our improvements to the super parsing approach using segmentation evaluation measures as well as the per-pixel rate and average per-class rate in a labelling evaluation. We demonstrate the success of our modified approach on the SIFT Flow dataset.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".