# ICFRNet: Image Complexity Prior Guided Feature Refinement for Real-time Semantic Segmentation

Source: [ICFRNet: Image Complexity Prior Guided Feature Refinement for Real-time Semantic Segmentation](<https://www.sota2.com/research/paper/68fa748d1e209131cea626b2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:12:36.753Z

Authors: Xin Zhang, Teodor Boyadzhiev, Jinglei Shi, Jufeng Yang

Published: 2024-08-25T08:42:24.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.13771>)

[PDF](<https://arxiv.org/pdf/2408.13771.pdf>)