论文标题
U2-ONET：具有多尺度注意机制的两级嵌套八度U结构，用于移动实例分割
U2-ONet: A Two-level Nested Octave U-structure with Multiscale Attention Mechanism for Moving Instances Segmentation
论文作者
论文摘要
实际应用程序中的大多数场景都是包含移动对象的动态场景，因此对许多计算机视觉应用程序进行精确移动对象至关重要。为了有效地分割场景中的所有移动对象，无论对象是否具有预定义的语义标签，我们都会提出一个具有多尺度注意机制的两级嵌套八度八度octave U结构网络，称为U2-inet。 U2-ONET的每个阶段都充满了我们新设计的八度剩余U块（ORSU），以增强在不同尺度上获取更多上下文信息的能力，同时降低特征图的空间冗余。为了有效地培训我们的多尺度深网，我们引入了层次结构培训策略，该策略在每个级别上计算损失，同时添加知识匹配损失以保持优化的一致性。实验结果表明，我们的方法在几个通用移动对象分割数据集中实现了最新性能。
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.
