Domain generalization (DG) aims at solving distribution shift problems in various scenes.
Existing approaches are based on Convolution Neural Networks (CNNs) or Vision …

Mamba is emerging as a novel approach to overcome the challenges faced by
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision …

Transformers have revolutionized the point cloud learning task, but the quadratic complexity
hinders its extension to long sequence and makes a burden on limited computational …

Transformers have revolutionized medical image restoration, but the quadratic complexity
still poses limitations for their application to high-resolution medical images. The recent …

Human-human motion generation is essential for understanding humans as social beings.
Although several transformer-based methods have been proposed, they typically model …

The Receptance Weighted Key Value (RWKV) model offers a novel alternative to the
Transformer architecture, merging the benefits of recurrent and attention-based systems …

The Segment Anything Model (SAM) is a foundational model for image segmentation tasks,
known for its strong generalization across diverse applications. However, its impressive …

Recent years, learned image compression has made tremendous progress to achieve
impressive coding efficiency. Its coding gain mainly comes from non-linear neural network …

Style transfer aims to generate a new image preserving the content but with the artistic
representation of the style source. Most of the existing methods are based on Transformers …

In recent years, deep neural networks, including Convolutional Neural Networks,
Transformers, and State Space Models, have achieved significant progress in Remote …