Results 31 to 40 of about 1,582,815 (295)

Supervised deep learning with vision transformer predicts delirium using limited lead EEG

Scientific Reports, 2023
As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool.
Malissa A. Mulkey, Huyunting Huang, Thomas Albanese, Sunghan Kim, Baijian Yang   +4 more
doaj   +1 more source

Semantic-Aware Local-Global Vision Transformer [PDF]

, 2022
Vision Transformers have achieved remarkable progresses, among which Swin Transformer has demonstrated the tremendous potential of Transformer for vision tasks.
Zhang, Jiatong, Lu, Guangming, Pei, Wenjie, Chen, Fanglin, Yao, Zengwei   +4 more
core  

Vision Transformer Adapters for Generalizable Multitask Learning [PDF]

, 2023
We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains.
Salzmann, Mathieu, Süsstrunk, Sabine, Bhattacharjee, Deblina; id_orcid   +2 more
core   +5 more sources

Optimal Topology of Vision Transformer for Real-Time Video Action Recognition in an End-To-End Cloud Solution

Machine Learning and Knowledge Extraction, 2023
This study introduces an optimal topology of vision transformers for real-time video action recognition in a cloud-based solution. Although model performance is a key criterion for real-time video analysis use cases, inference latency plays a more ...
Saman Sarraf, Milton Kabia
doaj   +1 more source

QuadTree Attention for Vision Transformers

CoRR, 2022
Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency. However, their quadratic computational complexity poses a major obstacle for applying them to vision tasks requiring dense predictions, such as object detection, feature matching, stereo, etc.
Tang, Shitao, Zhang, Jiahui, Zhu, Siyu, Tan, Ping   +3 more
openaire   +4 more sources

Towards Robust Vision Transformer [PDF]

, 2022
Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks.
Mao, Xiaofeng, Xue, Hui, He, Yuan, Chen, Yuefeng, Li, Xiaodan, Ye, Shaokai, Duan, Ranjie, Qi, Gege   +7 more
core  

Lightening-Transformer

, 2023
[HPCA24] Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer ...
Hanqing Zhu (17448903)
core   +1 more source

Semi-supervised Vision Transformers

, 2022
We study the training of Vision Transformers for semi-supervised image classification. Transformers have recently demonstrated impressive performance on a multitude of supervised learning tasks. Surprisingly, we show Vision Transformers perform significantly worse than Convolutional Neural Networks when only a small set of labeled data is available ...
Zejia Weng, Xitong Yang, Ang Li 0001, Zuxuan Wu, Yu-Gang Jiang 0001   +4 more
openaire   +3 more sources

AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows [PDF]

, 2023
Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities. To reduce the quadratic complexity caused by the attention, some outstanding work restricts attention to local regions or extends axial ...
Lin, Fangjian, Yu, Long, Ma, Yizhe, Wu, Sitong, Tian, Shengwei   +4 more
core  

$E(2)$-Equivariant Vision Transformer [PDF]

, 2023
Vision Transformer (ViT) has achieved remarkable performance in computer vision. However, positional encoding in ViT makes it substantially difficult to learn the intrinsic equivariance in data.
Yang, Kaifan, He, Fengxiang, Xu, Renjun, Liu, Ke   +3 more
core   +1 more source