FANet: Feature Amplification Network for Semantic Segmentation in Cluttered Background

TL;DR

FANet introduces a feature amplification network with novel modules to enhance semantic cues, improving segmentation accuracy in cluttered and translucent object scenarios, achieving state-of-the-art results on challenging datasets.

Contribution

The paper proposes a new feature amplification network with adaptive enhancement modules that effectively utilize semantic cues for complex scene segmentation.

Findings

Achieves state-of-the-art performance on ZeroWaste-f dataset.

Effectively captures scale variations and semantic cues in cluttered backgrounds.

Outperforms existing methods in challenging real-world scenarios.

Abstract

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.