KDTwin: Task-Aware Knowledge Distillation for Lightweight Multi-Task Driving Scene Segmentation
Efficient perception models are essential for real-time autonomous driving, where accuracy and computational cost must be carefully balanced. However, applying knowledge distillation to multi-task driving scene segmentation is challenging because drivable-area and lane segmentation exhibit different spatial characteristics and class imbalance. We propose KDTwin, a task-aware distillation framework for lightweight multi-task segmentation networks. The proposed method performs distillation at both the shared encoder and task-specific decoders. Encoder-level pairwise distillation transfers spatial relational knowledge to enhance the student's shared representation. For the decoders, we use a...
| Authors | Huy Che, Minh-Khoi Do, Dinh-Duy Phan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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