  Due to the problem of performance constraints of unsupervised video object
detection, its large-scale application is limited. In response to this pain
point, we propose another excellent method to solve this problematic point. By
incorporating motion characterization in unsupervised video object detection,
detection accuracy is improved while reducing the computational amount of the
network. The whole network structure consists of dual-stream network, motion
guidance module, and multi-scale progressive fusion module. The appearance and
motion representations of the detection target are obtained through a
dual-stream network. Then, the semantic features of the motion representation
are obtained through the local attention mechanism in the motion guidance
module to obtain the high-level semantic features of the appearance
representation. The multi-scale progressive fusion module then fuses the
features of different deep semantic features in the dual-stream network further
to improve the detection effect of the overall network. We have conducted
numerous experiments on the three datasets of DAVIS 16, FBMS, and ViSal. The
verification results show that the proposed method achieves superior accuracy
and performance and proves the superiority and robustness of the algorithm.
