77 citations · 207 across the 26 of their papers we have counts for
25 papers · 1 filter
SFMformer: A Spatial-Frequency Modulation Transformer for Lightweight Image Super-Resolution
Chih-Hsiang Yang, Chia-Min Lin, Ching-Yu Tsai +2
Sparse attention mechanisms, which score all token pairs but propagate only the strongest, now underpin the most efficient Transformers for lightweight image super-resolution. This…
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 must preserve thin, broken, and low-contrast strokes against degraded backgrounds; Mamba-based state space models have so far been explored only prelimi…
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…
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…
GlassesGB: Controllable 2D GAN-Based Eyewear Personalization for 3D Gaussian Blendshapes Head Avatars
Rui-Yang Ju, Jen-Shiun Chiang
Virtual try-on systems allow users to interactively try different products within VR scenarios. However, most existing VTON methods operate only on predefined eyewear templates and…
MFE-GAN: Efficient GAN-based Framework for Document Image Enhancement and Binarization with Multi-scale Feature Extraction
Rui-Yang Ju, KokSheik Wong, Yanlin Jin +1
Document image enhancement and binarization are commonly performed prior to document analysis and recognition tasks for improving the efficiency and accuracy of optical character r…
Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images
Rui-Yang Ju, Chun-Tse Chien, Enkaer Xieerke +1
Children often suffer wrist trauma in daily life, while they usually need radiologists to analyze and interpret X-ray images before surgical treatment by surgeons. The development…
YOLOv8-ResCBAM: YOLOv8 Based on An Effective Attention Module for Pediatric Wrist Fracture Detection
Rui-Yang Ju, Chun-Tse Chien, Jen-Shiun Chiang
Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, s…
Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection
Rui-Yang Ju, Chun-Tse Chien, Chia-Min Lin +1
Children often suffer wrist injuries in daily life, while fracture injuring radiologists usually need to analyze and interpret X-ray images before surgical treatment by surgeons. T…
Efficient Generative Adversarial Networks for Color Document Image Enhancement and Binarization Using Multi-scale Feature Extraction
Rui-Yang Ju, KokSheik Wong, Jen-Shiun Chiang
The outcome of text recognition for degraded color documents is often unsatisfactory due to interference from various contaminants. To extract information more efficiently for text…
FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method
Yanbing Bai, Siao Li, Rui-Yang Ju +3
Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing…
YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection
Chun-Tse Chien, Rui-Yang Ju, Kuang-Yi Chou +2
Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, s…
Semantic Segmentation Using Super Resolution Technique as Pre-Processing
Chih-Chia Chen, Wei-Han Chen, Jen-Shiun Chiang +2
Combining high-level and low-level visual tasks is a common technique in the field of computer vision. This work integrates the technique of image super resolution to semantic segm…
CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization
Rui-Yang Ju, Yu-Shian Lin, Jen-Shiun Chiang +3
To efficiently extract textual information from color degraded document images is a significant research area. The prolonged imperfect preservation of ancient documents has led to…
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
Rui-Yang Ju, Chih-Chia Chen, Jen-Shiun Chiang +3
The Transformer-based method has demonstrated remarkable performance for image super-resolution in comparison to the method based on the convolutional neural networks (CNNs). Howev…
Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks
Rui-Yang Ju, Yu-Shian Lin, Yanlin Jin +3
The efficient extraction of text information from the background in degraded color document images is an important challenge in the preservation of ancient manuscripts. The imperfe…
Connection Reduction of DenseNet for Image Recognition
Rui-Yang Ju, Jen-Shiun Chiang, Chih-Chia Chen +1
Convolutional Neural Networks (CNN) increase depth by stacking convolutional layers, and deeper network models perform better in image recognition. Empirical research shows that si…