Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

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 …

Network architecture plays a key role in the deep learning-based computer vision system.
The widely-used convolutional neural network and transformer treat the image as a grid or …

Vision transformers have been successfully applied to image recognition tasks due to their
ability to capture long-range dependencies within an image. However, there are still gaps in …

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 …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

In the field of computer vision, recent works show that a pure MLP architecture mainly
stacked by fully-connected layers can achieve competing performance with CNN and …

Abstract Knowledge distillation (KD) has proven to be a highly effective approach for
enhancing model performance through a teacher-student training scheme. However, most …

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 …