  Most scenes in practical applications are dynamic scenes containing moving
objects, so segmenting accurately moving objects is crucial for many computer
vision applications. In order to efficiently segment out all moving objects in
the scene, regardless of whether the object has a predefined semantic label, we
propose a two-level nested Octave U-structure network with a multiscale
attention mechanism called U2-ONet. Each stage of U2-ONet is filled with our
newly designed Octave ReSidual U-block (ORSU) to enhance the ability to obtain
more context information at different scales while reducing spatial redundancy
of feature maps. In order to efficiently train our multi-scale deep network, we
introduce a hierarchical training supervision strategy that calculates the loss
at each level while adding a knowledge matching loss to keep the optimization
consistency. Experimental results show that our method achieves
state-of-the-art performance in several general moving objects segmentation
datasets.
