Glass is very common in the real world. Influenced by the uncertainty about the glass region and the varying complex scenes behind the glass, the existence of glass poses severe challenges to many computer vision tasks, making glass segmentation as an important computer vision task. Glass does not have its own visual appearances but only transmit/reflect the appearances of its surroundings, making it fundamentally different from other common objects. To address such a challenging task, existing methods typically explore and combine useful cues from different levels of features in the deep network. As there exists a characteristic gap between level-different features, i.e., deep layer features embed more high-level semantics and are better at locating the target objects while shallow layer features have larger spatial sizes and keep richer and more detailed low-level information, fusing these features naively thus would lead to a sub-optimal solution. In this paper, we approach the effective features fusion towards accurate glass segmentation in two steps. First, we attempt to bridge the characteristic gap between different levels of features by developing a Discriminability Enhancement (DE) module which enables level-specific features to be a more discriminative representation, alleviating the features incompatibility for fusion. Second, we design a Focus-and-Exploration Based Fusion (FEBF) module to richly excavate useful information in the fusion process by highlighting the common and exploring the difference between level-different features.
翻译:玻璃在现实世界中非常常见。受玻璃区域不确定性和玻璃背后复杂场景不同的影响,玻璃的存在给许多计算机视觉任务带来严重挑战,使玻璃分割成为重要的计算机视觉任务。玻璃没有自己的视觉外观,而只是传播/反映周围的外观,使其与其他共同对象有根本的区别。为了应对这种具有挑战性的任务,现有方法通常探索并结合深网络不同层次地貌的有用线索。由于不同层次地貌之间有显著的差距,即深层地层地貌包含更高级别的语义,在定位目标物体方面更好,而浅层地貌的地貌则具有较大的空间大小,保持更丰富和更详细的低层次信息,因此,这些地貌只能传播/反映周围的外貌,从而导致亚优的解决方案。在本文中,我们从有效的特征接近于精确的玻璃分化,分两个步骤。首先,我们试图通过开发一个不同层次的增强(DE)模块,使不同层次的特性能够定位目标物体的定位,而更深层地层地标特征能够成为更深层的深层次的深层次的深层外观、缩小的深层地层空间的深层地层地段,从而缩小地分析,从而缩小地研究基础的深层层层层层层层地貌不相容。