PIDDNet: RGB-Depth Fusion Network for Real-time Semantic Segmentation
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
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.
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 | 0.000 |
| 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 | 0.001 |
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 it