A Strip Pooling Attention Network for Urban Scene Images Segmentation
Bibliographic record
Abstract
The field of urban scene image segmentation is a crucial task in the field of computer vision.Aiming at the problems of large parameter count and insufficient image segmentation accuracy of the traditional DeepLabV3+ model, an improved lightweight DeepLabV3+ model is designed.The overall performance of the model is improved by replacing the Xception backbone network with MobileNetV2, introducing the band pooling module and the densely connected null pyramid module in ASPP, and using the GD-FAM multi-feature fusion module in the fusion stage.Using Cityscapes as the dataset, the model experiment results show that compared with the traditional Deeplabv3+ model, this paper's method increases the target category IoUs of urban scenes such as pedestrians, cyclists, and columns by 3.1%, 4.41%, and 6.74%, respectively.Therefore, the segmentation effect of the model in this paper is significantly better than the segmentation effect of other models.The mIoU of the MobileNetV2 backbone network is 4.91% higher than the baseline model.The loss function change curve of the model shows that it tends to converge after 100 iterations.In summary, the overall segmentation performance of the improved model is significantly improved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".