A new robust context-based dense CRF model for image labeling
Bibliographic record
Abstract
Fully-connected conditional random fields (CRF) models have recently been developed for image labeling task to incorporate interactions of all pairs of pixels in the image. Efficient inference in fully-connected models is very sensitive to initialization of the unary potentials. In this paper, we propose a new robust context-based fully-connected CRF model which alleviates initialization sensitivity of inference in dense CRFs. The new model integrates an extra hidden node that accounts for the overall context of the image and is connected to all other pixel nodes. By incorporating the new context node in CRF graph, we maximize probability of labeling configuration jointly with the image's context. Therefore, wrong initializations of objects that contradict the overal context could be refined. We define the context-based unary and pairwise potentials and further derive the inference algorithm for the proposed model based on the mean field approximation method. We run experiments over the benchmark MSRC image database and demonstrate that the new model improves object recognition accuracy by about 21%. We show where the conventional CRF model is impeded by wrong initialization of unary potentials, the proposed model identifies the labels correctly.
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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 | 0.000 |
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".