Diversity augmentation and multi-fuzzy label for semi-supervised semantic segmentation
Bibliographic record
Abstract
Semantic segmentation aims to provide pixel-wise accurate predictions for images. Semi-supervised semantic segmentation aims to learn a semantic segmentation model using a limited number of labeled images and a large fraction of unlabeled images. Existing methods primarily focus on introducing additional models or complex training procedures but overlook the model itself and such complex strategies tend to discard many usable pixels, exacerbating the class imbalance problem . In this paper, we propose DAM for semi-supervised semantic segmentation, a simple yet effective method that mainly focuses on the inputs and outputs of the model itself. For the input component, we posit that diverse data augmentations can provide more semantic information . Therefore, we propose a method called Random Diversity Augmentations. Given an unlabeled image, we apply different triple-level data augmentations to provide more semantic information. For the output component, our approach is inspired by the fact that many unreliable predictions are confused only among the top classes rather than all classes, so we contend that fuzzy pixels can still provide valuable guidance to the model. Specifically, we select fuzzy pixels based on confidence and assign multi-fuzzy labels to these pixels for training the model, which allows us to leverage the information more effectively. Our straightforward DAM achieves new state-of-the-art performance on SSS different benchmarks. Code is available at https://github.com/Wang-zhenyan/DAM .
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".