ECASeg: Enhancing Semantic Segmentation with Edge Context and Attention Strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Semantic segmentation has been a cornerstone for several visual perception-based applications, including autonomous driving, remote sensing and geographic information systems (RS-GIS), and medical diagnostics. By accurately segmenting the input visuals, an intelligent agent can perceive the environment and perform target operations appropriately. The emergence of deep learning (DL) has contributed to the development of cutting-edge segmentation models. However, there is scope for continual improvement and model optimization of existing solutions. Thus, this work pragmatically integrates an attention mechanism and edge features to improve the semantic segmentation. The exhaustive experimental analysis on the benchmark CamVid driving dataset show that the proposed approach achieves a mean Intersection over Union (mIoU) of 66.53%, which is a 5.12% improvement compared to the performance of a baseline model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it