PIDDNet: RGB-Depth Fusion Network for Real-time Semantic Segmentation
Bibliographic record
Abstract
For RGB semantic segmentation, a two-branch network was proposed to effectively utilize both local detail information and global contextual information within an RGB image. This architecture combines a shallow spatial path with a deeper context path, resulting in high performance and FPS. Research on RGB-Depth segmentation has shown the performance gain that the depth map could provide complementary information to the RGB model. However, the advantage of fusing RGB and depth map within a two-branch network framework is unclear due to the distinct characteristics of these modalities. To address this, we present a novel fusion RGB-Depth architecture that takes into account the attributes of local context, global context, RGB, and depth map. Through the bidirectional image depth fusion technique, we effectively leverage each of the modalities, achieving a performance of 81.23 mIoU. This marks a gain of 1.27% when compared to the RGB-only model and 0.45% when contrasted with the element-wise feature addition fusion baseline.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".