Convolutional gated recurrent networks for video semantic segmentation in automated driving
Bibliographic record
Abstract
Semantic segmentation is an important visual perception module of automated driving. Most of the progress has been focused on single-frame image segmentation with an optional temporal post-processing. In this paper, we propose a novel algorithm to utilize the temporal information in the semantic segmentation model using convolutional gated recurrent networks. The main motivation is to design a spatio-temporal network which can leverage motion cues for aiding segmentation and providing temporally consistent results. The proposed algorithm makes use of a fully convolutional network (FCN) that is embedded into a gated recurrent architecture. We use FCN because of its simplicity and ease of extension and the embedding can extend to other architectures. We also chose an FCN model with reasonable computational complexity suitable for real-time applications. Experimental results show consistent accuracy improvements over the baseline FCN in several datasets and it is also visually evident in our test videos shared on YouTube. The accuracy improvements for binary segmentation using F-measure were 5% and 3% in SegTrack2 and Davis respectively and the improvements for semantic segmentation in mean IoU were 5.7% and 1.7% in Synthia and Camvid respectively. To our knowledge, no prior work has been done for CNN based spatio-temporal video segmentation for automated driving and we hope that our results encourage further research in this area.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".