State space models (SSM) with selection mechanisms and hardware-aware architectures,
namely Mamba, have recently shown significant potential in long-sequence modeling. Since …

Recently the state space models (SSMs) with efficient hardware-aware designs, ie, the
Mamba deep learning model, have shown great potential for long sequence modeling …

Mamba, a recent selective structured state space model, performs excellently on long
sequence modeling tasks. Mamba mitigates the modeling constraints of convolutional …

In the realm of medical image segmentation, both CNN-based and Transformer-based
models have been extensively explored. However, CNNs exhibit limitations in long-range …

Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

Transformers with linear attention allow for efficient parallel training but can simultaneously
be formulated as an RNN with 2D (matrix-valued) hidden states, thus enjoying linear (with …

Traditional convolutional neural networks have a limited receptive field while transformer-
based networks are mediocre in constructing long-term dependency from the perspective of …

Transformers have become one of the foundational architectures in point cloud analysis
tasks due to their excellent global modeling ability. However, the attention mechanism has …

Accurate medical image segmentation demands the integration of multi-scale information,
spanning from local features to global dependencies. However, it is challenging for existing …

Recent years have witnessed great progress in image restoration thanks to the
advancements in modern deep neural networks eg Convolutional Neural Network and …