A Vanilla Multi-Task Framework for Dense Visual Prediction Solution to
  1st VCL Challenge -- Multi-Task Robustness Track
Zehui Chen Qiuchen Wang Zhenyu Li Jiaming Liu Shanghang Zhang Feng Zhao
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.