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

U-Net has become a cornerstone in various visual applications such as image segmentation
and diffusion probability models. While numerous innovative designs and improvements …

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 …

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

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality
images from degraded inputs. Various models, such as convolutional neural networks …

Transformer has been extensively explored for hyperspectral image (HSI) classification.
However, transformer poses challenges in terms of speed and memory usage because of its …

Recently, infrared small-target detection (ISTD) has made significant progress, thanks to the
development of basic models. Specifically, the models combining CNNs with Transformers …

High-resolution remotely sensed images pose challenges to traditional semantic
segmentation networks, such as Convolutional Neural Networks (CNNs) and Vision …

UNet and its variants have been widely used in medical image segmentation. However,
these models, especially those based on Transformer architectures, pose challenges due to …

Abstract Referring Image Segmentation (RIS) leveraging transformers has achieved great
success on the interpretation of complex visual-language tasks. However, the quadratic …