77 citations · 207 across the 26 of their papers we have counts for
12 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…