Abstract Machine Learning (ML) and Deep Learning (DL) have achieved high success in
many textual, auditory, medical imaging, and visual recognition patterns. Concerning the …

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 …

Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-
tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers …

In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective
vision transformer architecture that is able to capture global context while maintaining …

MetaFormer, the abstracted architecture of Transformer, has been found to play a significant
role in achieving competitive performance. In this paper, we further explore the capacity of …

Transformers have shown great potential in computer vision tasks. A common belief is their
attention-based token mixer module contributes most to their competence. However, recent …

The visual feature pyramid has shown its superiority in both effectiveness and efficiency in a
variety of applications. However, current methods overly focus on inter-layer feature …

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 …

Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …

Vision Transformers have been the most popular network architecture in visual recognition
recently due to the strong ability of encode global information. However, its high …