Recently, the proposed deep multilayer perceptron (MLP) models have stirred up a lot of
interest in the vision community. Historically, the availability of larger datasets combined with …

Abstract Recently, visual Transformer (ViT) and its following works abandon the convolution
and exploit the self-attention operation, attaining a comparable or even higher accuracy than …

Recently, MLP-based vision backbones emerge. MLP-based vision architectures with less
inductive bias achieve competitive performance in image recognition compared with CNNs …

Pattern classification has always been essential in computer vision. Transformer paradigm
having attention mechanism with global receptive field in computer vision improves the …

Automatic segmentation of skin lesions is beneficial for improving the accuracy and
efficiency of melanoma diagnosis. However, due to variation in the size and shape of the …

Real-time video perception tasks are often challenging on resource-constrained edge
devices due to the issues of accuracy drop and hardware overhead, where saving …

Vision Transformers achieved outstanding performance in many computer vision tasks.
Early Vision Transformers such as ViT and DeiT adopt global self-attention, which is …

Recent studies show that self-attentions behave like low-pass filters (as opposed to
convolutions) and enhancing their high-pass filtering capability improves model …

Efficient and rational monitoring of plant health is an essential prerequisite for ensuring
optimal crop production and resource management in the field of agriculture. Computer …

Deep learning (DL) models, such as multilayer perceptrons (MLPs) and convolutional neural
networks (CNNs), have strong feature representation and nonlinear mapping capabilities …