3 papers
cs.CV2026
ATV-Net: Adaptive Triple-View Network with Dynamic Feature Fusion
Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen +3
Recent advances in semantic segmentation rely heavily on attention-based and transformer-style architectures that, while accurate, introduce considerable architectural complexity a…
cs.CV2026
Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation
Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen +3
CNN-based semantic segmentation networks usually rely on context heads such as ASPP, PPM, or attention modules to enlarge the receptive field. These heads are effective but may int…
cs.CV2026
FoR-Net: Learning to Focus on Hard Regions for Efficient Semantic Segmentation
Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen +4
We present FoR-Net, an efficient semantic segmentation framework that focuses on identifying and enhancing hard regions. Instead of relying on heavy global modeling, FoR-Net adopts…