From the 1 of 7 linked papers with an AI index.
7 papers
MambaPSA: A Mamba-based Replacement for C2PSA in YOLO26
Sheng-Wei Chan, Chia-Min Lin, Hsin-Jui Pan +4
The paper introduces MambaPSA, a lightweight Mamba‑based module that replaces the C2PSA block in the YOLO26 object detector and adds a bidirectional Vision Mamba (BiViM) to the nec…
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…
YOLO-AMC: An Improved YOLO Architecture with Attention Mechanisms for Building Crack Detection
Ching-Yu Tsai, Chia-Min Lin, Chih-Hsiang Yang +2
Crack detection plays an important role in infrastructure inspection and Structural Health Monitoring (SHM). However, cracks typically appear as thin, low-contrast structures and a…
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: 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…