Enhancing scene parsing by transferring structures via efficient low-rank graph matching
Why this work is in the frame
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Bibliographic record
Abstract
Scene parsing has attracted significant attention for its practical and theoretical value in computer vision. A typical scene parsing algorithm seeks to densely label pixels or 3-dimensional points from a scene. Traditionally, this procedure relies on a pre-trained classifier to identify the label information, and a smoothing step via Markov Random Field to enhance the consistency. LabelTranfer is a category of scene parsing algorithms to enhance traditional scene parsing framework, by finding dense correspondence and transferring labels across scenes. In this paper, we present a novel scene parsing algorithm which matches maximal similar structures between scenes via efficient low-rank graph matching. The inputs of the algorithm are images, and well- aligned point clouds if available. The images and the point clouds are processed in separate pipelines. The pipeline of images is to learn a reliable classifier and to match local structures via graph matching. The pipeline of point clouds is to conduct preliminary segmentation and to generate feasible label sets. The two pipelines are merged at inference step, in which we elaborate effective and efficient potential functions. We propose a new graph matching model incorporating low-rank and Frobenius regularization, which not only guarantees an accurate solution, but also provides high optimization efficiency via an eigen-decomposition strategy. Several challenging experiments are conducted, showing competitive performance of the proposed method compared to state-of-the-art LabelTransfer algorithm. Further, with point clouds, the performance can be significantly enhanced.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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