SS-DeepSeg: An Efficient Deeplab with Smart Scaling for Robust Semantic Segmentation
Bibliographic record
Abstract
Semantic segmentation is a critical task applied for scene understanding in computer vision with widespread applications in autonomous driving, remote sensing, and more. Despite DeepLabV3's strong segmentation capabilities, further improvements can enhance its computational efficiency and accuracy in complex scenarios, while handling the demands of realtime operation in resource-constrained environments. To address this, we propose three key enhancements to the DeepLabV3: (i) a lightweight backbone to reduce computational overhead while preserving feature extraction capabilities, (ii) a smart scaling strategy to enhance multi-scale feature aggregation and contextual understanding, and (iii) an atrous spatial pyramid pooling (ASPP) module augmented with attention for dynamic receptive field-based feature refinement. Comprehensive experiments on benchmark datasets validate the effectiveness of our approach, achieving a mean intersection over union (mIoU) of 73.78 % on the Cityscapes validation set -a 6.23% improvement over the baseline model. It also achieves a competitive 76.96 % on the CamVid test set, and 51.71 % on the LoveDA test set.
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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 | 0.001 |
| 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".