27 citations · 44 across the 14 of their papers we have counts for
14 papers
MSCA-UNet: Multi-Scale Context and Attention U-Net for Image Segmentation
Sheng-Wei Chan
U-Net remains a practical baseline for image segmentation because of its simple encoder-decoder structure and skip connections. However, the bottleneck representation is still domi…
MambaPSA: A Mamba-based Replacement for C2PSA in YOLO26
Sheng-Wei Chan, Chia-Min Lin, Hsin-Jui Pan +4
State space models (SSMs), notably Mamba, have recently emerged as efficient alternatives to self-attention with linear computational complexity. We investigate the integration of…
DR-Mamba: Automatic Inference-Time Domain Adaptation for Document Image Binarization via Sample-Conditioned Detail-Background Suppression
Sheng-Wei Chan, Jen-Shiun Chiang
Degraded document image binarization is sensitive to domain shifts caused by paper aging, bleed-through, stains, shadows, and uneven illumination, and the foreground-background sep…
Reload-Mamba: Hierarchical Anti-Dilution State-Space Modeling for Multi-Class Semantic Segmentation
Sheng-Wei Chan, Hsin-Jui Pan, Jen-Shiun Chiang
Mamba-based state space models offer linear-time long-range modeling for high-resolution dense prediction, but sequential state-space propagation can attenuate boundary-sensitive a…
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…
DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization
Sheng-Wei Chan, Yung-Che Wang, Hsin-Jui Pan +2
Document image binarization aims to separate foreground text from degraded backgrounds while preserving thin, broken, and low-contrast strokes. Although deep learning methods have…
ERN-Net : Evolving Reason Node-Net for Document Binarization
Hsin-Jui Pan, Sheng-Wei Chan, Jen-Shiung Chiang
This paper presents ERN-Net, an Evolving Reason Node-Net for efficient document image binarization. ERN-Net enhances degradation-sensitive regions, such as faint strokes, broken ch…
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…
FoR-Net: Focus-on-Regions Network for Semantic Segmentation
Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen +4
This paper presents Focus-on-Regions Network (FoR-Net), an efficient semantic segmentation framework that explicitly focuses on hard regions through a selector-driven Top-K mechani…
Breaking the Resource Wall: Geometry-Guided Sequence Modeling for Efficient Semantic Segmentation
Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen +3
High-performance semantic segmentation has achieved significant progress in recent years, often driven by increasingly large backbones and higher computational budgets. While effec…
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan +1
Lane detection plays an important role in a self-driving vehicle. Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can dist…
DSNet: An Efficient CNN for Road Scene Segmentation
Ping-Rong Chen, Hsueh-Ming Hang, Sheng-Wei Chan +1
Road scene understanding is a critical component in an autonomous driving system. Although the deep learning-based road scene segmentation can achieve very high accuracy, its compl…
Efficient Road Lane Marking Detection with Deep Learning
Ping-Rong Chen, Shao-Yuan Lo, Hsueh-Ming Hang +2
Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS). Limited by the onboard computing power, it is still a challenge…
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan +1
Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots. Most semantic segmentation research focuses on improving estimati…