2026· International Journal of Advanced Computer Science and Applications· Vol 17· 0 citations· 20 references
TL;DR
A Spectral Decoupling Adaptive Modulation module enhances low-frequency semantics and high-frequency details in the frequency domain, while a Hierarchical Spatial Dependency Modeling module captures local consistency and global semantic dependencies.
Abstract
To address the challenges of insufficient global semantic modeling and blurred boundaries in urban traffic scene segmentation, this study proposes a frequency-spatial collaborative framework based on DeepLabV3+. A Spectral Decoupling Adaptive Modulation (SDAM) module enhances low-frequency semantics and high-frequency details in the frequency domain. A Hierarchical Spatial Dependency Modeling (HSDM) module captures local consistency and global semantic dependencies, while a Structure-guided Adaptive Multi-scale Fusion (SAMF) module dynamically integrates multi-scale features using structural priors. Experiments on Cityscapes and CamVid demonstrate improvements of 3.6% and 1.6% over DeepLabV3+, respectively, while maintaining real-time performance.
Semantic segmentation of high-resolution remote sensing images faces three major challenges in frequency-spatial feature fusion: background clutter mixed into high-frequency components, semantic discontinuities within large homogeneous regions, and loss of fine rigid boundaries caused by convolutional downsampling. Tra...
Qi-Yuan Zhang, Jian-Shun Liu· Italian National Conference...· 0 citations
The complex spatial structures and varying scales of point cloud data make semantic segmentation a highly challenging task. While downsampling and feature fusion are essential steps in mainstream networks, existing models often suffer from local geometry loss and multiscale semantic conflicts during these processes. To...
Automated road extraction from high-resolution satellite imagery is critical for geospatial applications. However, accurate segmentation requires balancing global topological continuity with local boundary precision. Existing methods often struggle with this tradeoff, while directly adapting large-scale foundation mode...
Li-Lun Deng, Jiang-She Zhang, Hao-Wen Bai et al.· IEEE Journal of Selected Top...· 0 citations
Semantic segmentation is a fundamental task in remote sensing image (RSI) analysis and plays a vital role in applications such as land-use mapping, urban planning, and environmental monitoring. However, RSIs often suffer from severe grayscale variations and unstable interclass variance, which lead to blurred category b...
Hai-Long Ma, Song-Wei Pei, Bing-Feng Liu et al.· IEEE Transactions on Geoscie...· 0 citations
This work introduces S3VD, a Semantic-Guidance Spatio-Temporal Scanning framework for video deraining, featuring two key innovations: Multi-Scale Semantic Fusion (MSSF) Module and Spatio-Temporal Scanning Fusion (STSF) Module.
Kui Jiang, Yi'ang Chen, Yan Luo et al.· 0 citations
Real-time semantic segmentation aims to balance accuracy and inference effi-ciency, which remains a key challenge in computer vision. Although dual-resolution networks such as DDRNet achieve competitive performance, they are still limited by insufficient multi-scale context modeling, loss of high-frequency details caus...
Shuhui Zhu· Poster Volume 0007 The 2026...· 0 citations
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