Results 11 to 20 of about 1,683,190 (291)
Transformers in Vision: A Survey [PDF]
ACM Computing Surveys, 2022 Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks, e.g.,
Salman Khan +2 more
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Multiscale Vision Transformers [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Technical ...
Haoqi Fan 0001 +6 more
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Scaling Vision Transformers [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively.
Xiaohua Zhai +3 more
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Quantum Vision Transformers [PDF]
QuantumIn this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis.
El Amine Cherrat +5 more
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ViTFER: Facial Emotion Recognition with Vision Transformers
Applied System Innovation, 2022 In several fields nowadays, automated emotion recognition has been shown to be a highly powerful tool. Mapping different facial expressions to their respective emotional states is the main objective of facial emotion recognition (FER).
Aayushi Chaudhari +3 more
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Self-attention in vision transformers performs perceptual grouping, not attention
Frontiers in Computer Science, 2023 Recently, a considerable number of studies in computer vision involve deep neural architectures called vision transformers. Visual processing in these models incorporates computational models that are claimed to implement attention mechanisms. Despite an
Paria Mehrani, John K. Tsotsos
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Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers [PDF]
CAAI Artificial Intelligence Research, 2023 Most polyp segmentation methods use convolutional neural networks (CNNs) as their backbone, leading to two key issues when exchanging information between the encoder and decoder: (1) taking into account the differences in contribution between different ...
Bo Dong +5 more
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Vision Transformers for Remote Sensing Image Classification
Remote Sensing, 2021 In this paper, we propose a remote-sensing scene-classification method based on vision transformers. These types of networks, which are now recognized as state-of-the-art models in natural language processing, do not rely on convolution layers as in ...
Yakoub Bazi +4 more
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Art authentication with vision transformers
Neural Computing and Applications, 2023 AbstractIn recent years, transformers, initially developed for language, have been successfully applied to visual tasks. Vision transformers have been shown to push the state of the art in a wide range of tasks, including image classification, object detection, and semantic segmentation.
Eric Postma, Postma Eric
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Vision Transformers in medical computer vision—A contemplative retrospection
Engineering Applications of Artificial Intelligence, 2023 Recent escalation in the field of computer vision underpins a huddle of algorithms with the magnificent potential to unravel the information contained within images. These computer vision algorithms are being practised in medical image analysis and are transfiguring the perception and interpretation of Imaging data.
Muhammad Moazam Fraz
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