A Composite Dual Attention Transformer for Query-Based Instance Segmentation in Autonomous Driving
Bibliographic record
Abstract
High-precision instance segmentation is a fundamental requirement for applications such as autonomous driving, where accurately identifying objects like pedestrians and vehicles is essential. To address the limitations of existing models in capturing complex features and global context, we propose an enhanced QueryInst framework. Our approach integrates three key architectural advancements. We first employ a Dual Attention Vision Transformer (DaViT) backbone to extract rich, multiscale feature representations. This is further strengthened by a Composite Backbone Network (CBNet) architecture, which harnesses the output of multiple DaViT backbones to construct a more powerful and representative feature hierarchy. A Feature Pyramid Network with CARAFE upsampling is then used to enhance multi-scale feature fusion and recover fine spatial details. Extensive experiments on the Cityscapes dataset demonstrate the superior accuracy of our model, achieving an AP of 38.2% and an$\text{AP}_{50}$of$65.6 \%$. These results signify improvements of$3.8 \%$and 6% over the baseline, outperforming other state-of-the-art methods.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 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".