<ul><li>Depth estimation and scene segmentation are crucial tasks in intelligent transportation systems, and joint modeling of these tasks can reduce storage and training requirements.</li><li>This work introduces an adaptive multi-task distillation method to enhance unified modeling by dynamically adjusting the knowledge transfer from multiple teachers based on the student's learning ability.</li><li>To prevent knowledge forgetfulness during distillation with multiple teachers, a knowledge trajectory is proposed to maintain essential information learned by the model. This trajectory-based distillation loss helps guide the student model effectively.</li><li>Evaluation on benchmark datasets like Cityscapes and NYU-v2 shows that the proposed method outperforms existing solutions, with the code available in the supplementary materials.</li></ul>

JointDistill: Adaptive Multi-Task Distillation for Joint Depth Estimation and Scene Segmentation

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