In this report, we present our solution to the multi-task robustness track of
the 1st Visual Continual Learning (VCL) Challenge at ICCV 2023 Workshop. We
propose a vanilla framework named UniNet that seamlessly combines various
visual perception algorithms into a multi-task model. Specifically, we choose
DETR3D, Mask2Former, and BinsFormer for 3D object detection, instance
segmentation, and depth estimation tasks, respectively. The final submission is
a single model with InternImage-L backbone, and achieves a 49.6 overall score
(29.5 Det mAP, 80.3 mTPS, 46.4 Seg mAP, and 7.93 silog) on SHIFT validation
set. Besides, we provide some interesting observations in our experiments which
may facilitate the development of multi-task learning in dense visual
prediction.

我们提出的 UniNet 框架可以将 DETR3D、Mask2Former 和 BinsFormer 无缝地结合到一个多任务模型中，实现多任务鲁棒性，并在 1st Visual Continual Learning（VCL）挑战的多任务鲁棒性领域取得了显著性能。