  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.
