3 citations · 6 across the 7 of their papers we have counts for
7 papers
Physics and Data Driven Transformer-Mamba Framework for Flow Field
Zhuo Zhang, Shun Zou, Canqun Yang +1
While deep learning accelerates expensive partial differential equation solving in computational fluid dynamics (CFD), existing methods like PINNs and FNOs often struggle with gene…
Cross Paradigm Representation and Alignment Transformer for Image Deraining
Shun Zou, Yi Zou, Juncheng Li +2
Transformer-based networks have achieved strong performance in low-level vision tasks like image deraining by utilizing spatial or channel-wise self-attention. However, irregular r…
Learning Dual-Domain Multi-Scale Representations for Single Image Deraining
Shun Zou, Yi Zou, Mingya Zhang +3
Existing image deraining methods typically rely on single-input, single-output, and single-scale architectures, which overlook the joint multi-scale information between external an…
Fraesormer: Learning Adaptive Sparse Transformer for Efficient Food Recognition
Shun Zou, Yi Zou, Mingya Zhang +3
In recent years, Transformer has witnessed significant progress in food recognition. However, most existing approaches still face two critical challenges in lightweight food recogn…
SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance
Shun Zou, Mingya Zhang, Bingjian Fan +2
Skin lesion segmentation is a crucial method for identifying early skin cancer. In recent years, both convolutional neural network (CNN) and Transformer-based methods have been wid…
OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation
Shun Zou, Zhuo Zhang, Guangwei Gao
Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glau…
MambaMIC: An Efficient Baseline for Microscopic Image Classification with State Space Models
Shun Zou, Zhuo Zhang, Yi Zou +1
In recent years, CNN and Transformer-based methods have made significant progress in Microscopic Image Classification (MIC). However, existing approaches still face the dilemma bet…