Dense Image Labeling Using Deep Convolutional Neural Networks
Bibliographic record
Abstract
We consider the problem of dense image labeling. In recent work, many state-of-the-art techniques make use of Deep Convolutional Neural Networks (DCNN) for dense image labeling tasks (e.g. multi-class semantic segmentation) given their capacity to learn rich features. In this paper, we propose a dense image labeling approach based on DCNNs coupled with a support vector classifier. We employ the classifier based on DCNNs outputs while leveraging features corresponding to a variety of different labels drawn from a number of different datasets with distinct objectives for prediction. The principal motivation for using a support vector classifier is to explore the strength of leveraging different types of representations for predicting class labels, that are not directly related to the target task (e.g. predicted scene geometry may help assigning object labels). This is the first approach where DCNNs with predictions tied to different objectives are combined to produce better segmentation results. We evaluate our model on the Stanford background (semantic, geometric) and PASCAL VOC 2012 datasets. Compared to other state-of-the-art techniques, our approach produces state-of-the-art results for the Stanford background dataset, and also demonstrates the utility of making use of intelligence tied to different sources of labeling in improving upon baseline PASCAL VOC 2012 results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".