Add to ORKGVision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit their generalization. Previous compression algorithms usually start from the pre-trained dense models and only focus on efficient inference, while time-consuming training is still unavoidable. In contrast, this paper points out that the million-scale training data is redundant, which is the fundamental reason for the tedious training. To address the issue, this paper aims to introduce sparsity into data and proposes an end-to-end efficient training framework from three sparse perspectives, dubbed Tri-Level E-ViT. Specifically, we leverage a hierarchical data redundan...
Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been prove...
Vision Transformers (ViTs) with self-attention modules have recently achieved great empirical succes...
Sparsifying the Transformer has garnered considerable interest, as training the Transformer is very ...
Vision transformers (ViTs) have recently obtained success in many applications, but their intensive ...
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range ...
Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of...
Structural re-parameterization is a general training scheme for Convolutional Neural Networks (CNNs)...
Although transformer networks are recently employed in various vision tasks with outperforming perfo...
Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural ...
Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tack...
Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision tasks. Howev...
We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratic...
The transformer models have shown promising effectiveness in dealing with various vision tasks. Howe...
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural ...
This paper investigates two techniques for developing efficient self-supervised vision transformers ...
Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been prove...
Vision Transformers (ViTs) with self-attention modules have recently achieved great empirical succes...
Sparsifying the Transformer has garnered considerable interest, as training the Transformer is very ...
Vision transformers (ViTs) have recently obtained success in many applications, but their intensive ...
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range ...
Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of...
Structural re-parameterization is a general training scheme for Convolutional Neural Networks (CNNs)...
Although transformer networks are recently employed in various vision tasks with outperforming perfo...
Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural ...
Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tack...
Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision tasks. Howev...
We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratic...
The transformer models have shown promising effectiveness in dealing with various vision tasks. Howe...
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural ...
This paper investigates two techniques for developing efficient self-supervised vision transformers ...
Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been prove...
Vision Transformers (ViTs) with self-attention modules have recently achieved great empirical succes...
Sparsifying the Transformer has garnered considerable interest, as training the Transformer is very ...