ICFRNet: Image Complexity Prior Guided Feature Refinement for Real-time Semantic Segmentation
This work addresses the problem of accurate real-time segmentation for applications like autonomous driving, but it is incremental as it builds on existing attention-based methods.
The paper tackles real-time semantic segmentation by using image complexity as a prior to refine features, achieving higher accuracy on Cityscapes and CamViD datasets with competitive efficiency.
In this paper, we leverage image complexity as a prior for refining segmentation features to achieve accurate real-time semantic segmentation. The design philosophy is based on the observation that different pixel regions within an image exhibit varying levels of complexity, with higher complexities posing a greater challenge for accurate segmentation. We thus introduce image complexity as prior guidance and propose the Image Complexity prior-guided Feature Refinement Network (ICFRNet). This network aggregates both complexity and segmentation features to produce an attention map for refining segmentation features within an Image Complexity Guided Attention (ICGA) module. We optimize the network in terms of both segmentation and image complexity prediction tasks with a combined loss function. Experimental results on the Cityscapes and CamViD datasets have shown that our ICFRNet achieves higher accuracy with a competitive efficiency for real-time segmentation.