Vision Transformers have shown state-of-the-art results for various visual recognition tasks.
The dot-product self-attention mechanism that replaces convolution to mix spatial …

This paper studies the problem of designing compact binary architectures for vision multi-
layer perceptrons (MLPs). We provide extensive analysis on the difficulty of binarizing vision …

For the past ten years, CNN has reigned supreme in the world of computer vision, but
recently, Transformer has been on the rise. However, the quadratic computational cost of …

There are few studies on cervical ultrasound lymph-node-level classification which is very
important for qualitative diagnosis and surgical treatment of diseases. Currently, ultrasound …

Crowd anomaly detection is one of the most popular topics in computer vision in the context
of smart cities. A plethora of deep learning methods have been proposed that generally …

This paper presents ActiveMLP, a general MLP-like backbone for computer vision. The three
existing dominant network families, ie, CNNs, Transformers and MLPs, differ from each other …

Modeling spatial relationship in the data remains critical across many different tasks, such as
image classification, semantic segmentation and protein structure understanding. Previous …

Vision Transformers have shown great promise recently for many vision tasks due to the
insightful architecture design and attention mechanism. By revisiting the self-attention …

Choroid plexuses (CP) are structures of the brain ventricles which produce most of the
cerebrospinal fluid (CSF). Several postmortem and in vivo studies have pointed towards …

Background: Dermoscopic image segmentation using deep learning algorithms is a critical
technology for skin cancer detection and therapy. Specifically, this technology is a spatially …