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 …

We propose focal modulation networks (FocalNets in short), where self-attention (SA) is
completely replaced by a focal modulation module for modeling token interactions in vision …

Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image
patches as input, making them inflexible for different input sizes and hard to capture spatial …

MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and
Transformer. Despite its simplicity compared to Transformer, the concept of channel-mixing …

In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly
revolutionized various architectural design efforts: ViT achieved state-of-the-art image …

Recently, MLP-like vision models have achieved promising performances on mainstream
visual recognition tasks. In contrast with vision transformers and CNNs, the success of MLP …

Multimodal sentiment analysis aims to judge the sentiment of multimodal data uploaded by
the Internet users on various social media platforms. On one hand, existing studies focus on …

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

Abstract Convolutional Neural Networks (CNNs) have been regarded as the go-to models
for visual recognition. More recently, convolution-free networks, based on multi-head self …

Despite the remarkable capabilities of deep neural networks in image recognition, the
dependence on activation functions remains a largely unexplored area and has yet to be …