MAF-DeepLab: A Multiscale Attention Fusion Network for Semantic Segmentation
Bibliographic record
Abstract
The existing semantic segmentation networks mostly focus on extracting and expressing deep image features. But none of them could adequately aggregate contextual information, or utilize features on different scales or layers. To improve prediction results, this paper proposes a multiscale attention fusion network for semantic segmentation (MAF-DeepLab), which highlights important features, and aggregates multi-scale features well. Firstly, the high-level semantic features and low-level texture features were captured by a lightweight feature extraction network. Secondly, cascaded spatial pyramidal pooling (CSPP) were employed to fuse feature extraction branches with different receptive fields, enhancing the correlation between multi-scale features. Finally, a bottom-up attention fusion module was adopted to guide the cascading aggregation of high-level and low-level features, producing detailed saliency maps. MAF-DeepLab achieved an excellent effect of semantic segmentation on two benchmark datasets: CamVid (74.8%) and Cityscapes (83.4%).
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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 | 0.001 | 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) | 0.001 | 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".