Vision Transformers are very popular nowadays due to their state-of-the-art performance in several computer vision tasks, such as image classification and action recognition. Although their performance has been greatly enhanced through highly descriptive patch embeddings and hierarchical structures, there is still limited research on utilizing additional data representations so as to refine the selfattention map of a Transformer. To address this problem, a novel attention mechanism, called multi-manifold multihead attention, is proposed in this work to substitute the vanilla self-attention of a Transformer. The proposed mechanism models the input space in three distinct manifolds, namely Euclidean, Symmetric Positive Definite and Grassmann,...
Transformers have recently shown superior performances on various vision tasks. The large, sometimes...
This paper tackles the low-efficiency flaw of the vision transformer caused by the high computationa...
Vision transformers (ViT) have demonstrated impressive performance across numerous machine vision ta...
Vision transformers have shown excellent performance in computer vision tasks. As the computation co...
As the key component in Transformer models, attention mechanism has shown its great power in learnin...
Recent studies show that Vision Transformers(ViTs) exhibit strong robustness against various corrupt...
Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkab...
Vision Transformers achieved outstanding performance in many computer vision tasks. Early Vision Tra...
Transformer models are revolutionizing machine learning, but their inner workings remain mysterious....
Transformer, first applied to the field of natural language processing, is a type of deep neural net...
Transformers are the state-of-the-art for machine translation and grammar error correction. One of t...
Transformer-based methods have shown impressive performance in low-level vision tasks, such as image...
Transformer design is the de facto standard for natural language processing tasks. The success of th...
Although transformer networks are recently employed in various vision tasks with outperforming perfo...
Vision transformers have become popular as a possible substitute to convolutional neural networks (C...
Transformers have recently shown superior performances on various vision tasks. The large, sometimes...
This paper tackles the low-efficiency flaw of the vision transformer caused by the high computationa...
Vision transformers (ViT) have demonstrated impressive performance across numerous machine vision ta...
Vision transformers have shown excellent performance in computer vision tasks. As the computation co...
As the key component in Transformer models, attention mechanism has shown its great power in learnin...
Recent studies show that Vision Transformers(ViTs) exhibit strong robustness against various corrupt...
Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkab...
Vision Transformers achieved outstanding performance in many computer vision tasks. Early Vision Tra...
Transformer models are revolutionizing machine learning, but their inner workings remain mysterious....
Transformer, first applied to the field of natural language processing, is a type of deep neural net...
Transformers are the state-of-the-art for machine translation and grammar error correction. One of t...
Transformer-based methods have shown impressive performance in low-level vision tasks, such as image...
Transformer design is the de facto standard for natural language processing tasks. The success of th...
Although transformer networks are recently employed in various vision tasks with outperforming perfo...
Vision transformers have become popular as a possible substitute to convolutional neural networks (C...
Transformers have recently shown superior performances on various vision tasks. The large, sometimes...
This paper tackles the low-efficiency flaw of the vision transformer caused by the high computationa...
Vision transformers (ViT) have demonstrated impressive performance across numerous machine vision ta...