Results 41 to 50 of about 1,683,190 (291)
Rethinking Spatial Dimensions of Vision Transformers [PDF]
IEEE International Conference on Computer Vision, 2021 Vision Transformer (ViT) extends the application range of transformers from language processing to computer vision tasks as being an alternative architecture against the existing convolutional neural networks (CNN).
Byeongho Heo +5 more
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Multi-Manifold Attention for Vision Transformers
IEEE Access, 2023 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
Dimitrios Konstantinidis +3 more
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Separable Self-attention for Mobile Vision Transformers [PDF]
Trans. Mach. Learn. Res., 2022 Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across several mobile vision tasks, including classification and detection.
Sachin Mehta, Mohammad Rastegari
semanticscholar +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 code: https://github.com/OpenNLPLab/Vicinity-Vision ...
Weixuan Sun +9 more
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TinyViT: Fast Pretraining Distillation for Small Vision Transformers [PDF]
European Conference on Computer Vision, 2022 Vision transformer (ViT) recently has drawn great attention in computer vision due to its remarkable model capability. However, most prevailing ViT models suffer from huge number of parameters, restricting their applicability on devices with limited ...
Kan Wu +6 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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Recurrent Vision Transformers for Object Detection with Event Cameras [PDF]
Computer Vision and Pattern Recognition, 2022 We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with submillisecond latency at a high-dynamic range and with strong robustness against motion blur.
Mathias Gehrig, Davide Scaramuzza
semanticscholar +1 more source
Transformers in Remote Sensing: A Survey
Remote Sensing, 2023 Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer-based architectures, originally introduced in natural language processing, have pervaded ...
Abdulaziz Amer Aleissaee +6 more
doaj +1 more source
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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CompletionFormer: Depth Completion with Convolutions and Vision Transformers [PDF]
Computer Vision and Pattern Recognition, 2023 Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth prediction.
Youming Zhang +5 more
semanticscholar +1 more source