Existing deep learning approaches leave out the semantic cues that are crucial in semantic segmentation present in complex scenarios including cluttered backgrounds and translucent objects, etc. To handle these challenges, we propose a feature amplification network (FANet) as a backbone network that incorporates semantic information using a novel feature enhancement module at multi-stages. To achieve this, we propose an adaptive feature enhancement (AFE) block that benefits from both a spatial context module (SCM) and a feature refinement module (FRM) in a parallel fashion. SCM aims to exploit larger kernel leverages for the increased receptive field to handle scale variations in the scene. Whereas our novel FRM is responsible for generating semantic cues that can capture both low-frequency and high-frequency regions for better segmentation tasks. We perform experiments over challenging real-world ZeroWaste-f dataset which contains background-cluttered and translucent objects. Our experimental results demonstrate the state-of-the-art performance compared to existing methods.

翻译：现有的深度学习方法在复杂场景（如杂乱背景和半透明物体等）的语义分割中，往往忽略了关键的语义线索。为应对这些挑战，我们提出了一种特征增强网络（FANet）作为骨干网络，该网络通过在多阶段引入新颖的特征增强模块来融合语义信息。为此，我们设计了一种自适应特征增强（AFE）模块，该模块以并行方式结合了空间上下文模块（SCM）与特征细化模块（FRM）的优势。SCM旨在利用更大的卷积核来扩大感受野，以处理场景中的尺度变化；而我们提出的新型FRM则负责生成能够同时捕捉低频与高频区域语义线索的特征，以提升分割性能。我们在包含背景杂乱及半透明物体的真实世界挑战性数据集ZeroWaste-f上进行了实验，结果表明我们的方法相较于现有技术达到了最先进的性能水平。