ExFuse: Enhancing Feature Fusion for Semantic Segmentation
This addresses performance limitations in semantic segmentation for computer vision applications, representing an incremental improvement over existing fusion methods.
The paper tackles the problem of ineffective feature fusion in semantic segmentation by proposing ExFuse, a framework that bridges the semantic and resolution gap between low-level and high-level features, improving segmentation quality by 4.0% and achieving 87.9% mean IoU on PASCAL VOC 2012.
Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we first point out that a simple fusion of low-level and high-level features could be less effective because of the gap in semantic levels and spatial resolution. We find that introducing semantic information into low-level features and high-resolution details into high-level features is more effective for the later fusion. Based on this observation, we propose a new framework, named ExFuse, to bridge the gap between low-level and high-level features thus significantly improve the segmentation quality by 4.0\% in total. Furthermore, we evaluate our approach on the challenging PASCAL VOC 2012 segmentation benchmark and achieve 87.9\% mean IoU, which outperforms the previous state-of-the-art results.