Results 21 to 30 of about 25,505 (263)
Sensors, 2023 Understanding actions in videos remains a significant challenge in computer vision, which has been the subject of several pieces of research in the last decades.
Oumaima Moutik +6 more
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International Journal of Computer Vision, 2023 We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only one ViT model at a time, but is capable of providing improved image recognition performance with various computational costs. Here, the trained ViT model, termed super vision
Mingbao Lin +6 more
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Transformer-based ripeness segmentation for tomatoes
Smart Agricultural Technology, 2023 With the recent development of computer vision technology, various computer vision techniques have been applied to agriculture. Recently, the Transformer network has been introduced to image recognition, which allows a different approach to extracting ...
Risa Shinoda +3 more
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Transformer architectures for computer vision: A comprehensive review and future research directions [PDF]
EPJ Web of ConferencesLong-range dependencies and contextual relationships in videos were captured by using Convolutional Neural Networks (CNNs) in past. Recently the use of Transformers is started for capturing the long-range dependencies and contextual relationships in ...
Ugile Tukaram, Uke Nilesh
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 code: https://github.com/OpenNLPLab/Vicinity-Vision ...
Weixuan Sun +9 more
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Vision Transformers Are Robust Learners
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy. What remains largely unexplored is their robustness evaluation and attribution.
Sayak Paul, Pin-Yu Chen
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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.
Schaerf, Ludovica +2 more
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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 +4 more
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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
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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 +3 more
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