We propose a novel multi-task learning system that combines appearance and
motion cues for a better semantic reasoning of the environment. A unified
architecture for joint vehicle detection and motion segmentation is introduced.
In this architecture, a two-stream encoder is shared among both tasks. In order
to evaluate our method in autonomous driving setting, KITTI annotated sequences
with detection and odometry ground truth are used to automatically generate
static/dynamic annotations on the vehicles. This dataset is called KITTI Moving
Object Detection dataset (KITTI MOD). The dataset will be made publicly
available to act as a benchmark for the motion detection task. Our experiments
show that the proposed method outperforms state of the art methods that utilize
motion cue only with 21.5% in mAP on KITTI MOD. Our method performs on par with
the state of the art unsupervised methods on DAVIS benchmark for generic object
segmentation. One of our interesting conclusions is that joint training of
motion segmentation and vehicle detection benefits motion segmentation. Motion
segmentation has relatively fewer data, unlike the detection task. However, the
shared fusion encoder benefits from joint training to learn a generalized
representation. The proposed method runs in 120 ms per frame, which beats the
state of the art motion detection/segmentation in computational efficiency.

该文章提出了一种新型的多任务学习系统，将外观和运动线索相结合，以更好地对环境进行语义推理，其中介绍了一种联合车辆检测和运动分割的统一架构，并使用 KITTI 数据集评估了该方法，在运动检测任务上的性能优于其他利用运动线索方法 21.5％，在通用物体分割任务上表现与现有的无监督方法相当，其中一个有趣的结论是运动分割与车辆检测的联合训练有益于运动分割