12 citations · 19 across the 8 of their papers we have counts for
4 papers · 1 filter
SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation
Yunxiang Fu, Meng Lou, Yizhou Yu
High-quality semantic segmentation relies on three key capabilities: global context modeling, local detail encoding, and multi-scale feature extraction. However, recent methods str…
LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba
Yunxiang Fu, Chaoqi Chen, Yizhou Yu
Recent Transformer-based diffusion models have shown remarkable performance, largely attributed to the ability of the self-attention mechanism to accurately capture both global and…
SparX: A Sparse Cross-Layer Connection Mechanism for Hierarchical Vision Mamba and Transformer Networks
Meng Lou, Yunxiang Fu, Yizhou Yu
Due to the capability of dynamic state space models (SSMs) in capturing long-range dependencies with linear-time computational complexity, Mamba has shown notable performance in NL…
DreamDA: Generative Data Augmentation with Diffusion Models
Yunxiang Fu, Chaoqi Chen, Yu Qiao +1
The acquisition of large-scale, high-quality data is a resource-intensive and time-consuming endeavor. Compared to conventional Data Augmentation (DA) techniques (e.g. cropping and…