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 …

As a powerful modelling method, piecewise linear neural networks (PWLNNs) have proven
successful in various fields, most recently in deep learning. To apply PWLNN methods, both …

Recent studies show that Vision Transformers (ViTs) exhibit strong robustness against
various corruptions. Although this property is partly attributed to the self-attention …

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 …

Recent vision transformers, large-kernel CNNs and MLPs have attained remarkable
successes in broad vision tasks thanks to their effective information fusion in the global …

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 …

Mental health problems are an increasingly common social issue severely affecting health
and well-being. Multimedia processing technologies via facial expression show appealing …

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 …

Recent advances in deep learning have mainly relied on Transformers due to their data
dependency and ability to learn at scale. The attention module in these architectures …

With the increasing demand for video understanding, video moment and highlight detection
(MHD) has emerged as a critical research topic. MHD aims to localize all moments and …