3 papers
cs.CV2025
A2Mamba: Attention-augmented State Space Models for Visual Recognition
Meng Lou, Yunxiang Fu, Yizhou Yu
Transformers and Mamba, initially invented for natural language processing, have inspired backbone architectures for visual recognition. Recent studies integrated Local Attention T…
cs.CV2024
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…
cs.CV2024
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…