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U2-ONet A Two-level Nested Octave U-structure with Multiscale Attention Mechanism for Moving Instances Segmentation

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Document pages: 10 pages

Abstract: Most scenes in practical applications are dynamic scenes containing movingobjects, so segmenting accurately moving objects is crucial for many computervision applications. In order to efficiently segment out all moving objects inthe scene, regardless of whether the object has a predefined semantic label, wepropose a two-level nested Octave U-structure network with a multiscaleattention mechanism called U2-ONet. Each stage of U2-ONet is filled with ournewly designed Octave ReSidual U-block (ORSU) to enhance the ability to obtainmore context information at different scales while reducing spatial redundancyof feature maps. In order to efficiently train our multi-scale deep network, weintroduce a hierarchical training supervision strategy that calculates the lossat each level while adding a knowledge matching loss to keep the optimizationconsistency. Experimental results show that our method achievesstate-of-the-art performance in several general moving objects segmentationdatasets.

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