5 papers
Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
Meng Lou, Yunxiang Fu, Yizhou Yu
Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent years. However, how…
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…
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…
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…
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…