Results 11 to 20 of about 25,011 (267)
A Survey on Vision Transformer [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities, researchers are looking at ways to apply transformer to computer vision tasks.
Xinghao Chen +2 more
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Review of Transformer in Computer Vision [PDF]
Jisuanji kexue, 2023 Transformer is an attention-based encoder-decoder architecture.Due to its long-range sequence modeling and parallel computing capability,Transformer have made a significant breakthrough in natural language processing and is gradually expanding to ...
CHEN Luoxuan, LIN Chengchuang, ZHENG Zhaoliang, MO Zefeng, HUANG Xinyi, ZHAO Gansen
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ViTT: Vision Transformer Tracker
Sensors, 2021 This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV).
Xiaoning Zhu +4 more
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Remote Sensing, 2023 Efficient and accurate rice identification based on high spatial and temporal resolution remote sensing imagery is essential for achieving precision agriculture and ensuring food security.
Huiyao Xu, Jia Song, Yunqiang Zhu
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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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CSiT: A Multiscale Vision Transformer for Hyperspectral Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 The hyperspectral image (HSI) has nearly continuous spectral information; thus, the target of interest can be accurately identified by the subtle details of spectral properties.
Wenxuan He +4 more
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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 H. Khan 0001 +5 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Prior works have proposed several strategies to reduce the computational cost of self-attention mechanism. Many of these works consider decomposing the self-attention procedure into regional and local feature extraction procedures that each incurs a much smaller computational complexity.
Ting Yao 0003 +5 more
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Sensors, 2023 Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images.
Jia Song, A-Xing Zhu, Yunqiang Zhu
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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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