Results 21 to 30 of about 18,617 (276)
Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
Applied Sciences, 2022 Transformer complements convolutional neural network (CNN) has achieved better performance than improved CNN-based methods. Specially, Transformer is utilized to be combined with U-shaped structure, skip-connections, encoder, and even them all together ...
Zhihao Liao, Neng Fan, Kai Xu
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Entropy, 2022 Convolutional neural networks have long dominated semantic segmentation of very-high-resolution (VHR) remote sensing (RS) images. However, restricted by the fixed receptive field of convolution operation, convolution-based models cannot directly obtain ...
Yufen Xu, Shangbo Zhou, Yuhui Huang
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A Swin transformer and MLP based method for identifying cherry ripeness and decay
Frontiers in Physics, 2023 Cherries are a nutritionally beneficial and economically significant crop, with fruit ripeness and decay (rot or rupture) being critical indicators in the cherry sorting process.
Ke Song, Jiwen Yang, Guohui Wang
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Gaze Estimation Based on Convolutional Structure and Sliding Window-Based Attention Mechanism
Sensors, 2023 The direction of human gaze is an important indicator of human behavior, reflecting the level of attention and cognitive state towards various visual stimuli in the environment.
Yujie Li +4 more
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Swin-Pose: Swin Transformer Based Human Pose Estimation
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR), 2022 Convolutional neural networks (CNNs) have been widely utilized in many computer vision tasks. However, CNNs have a fixed reception field and lack the ability of long-range perception, which is crucial to human pose estimation. Due to its capability to capture long-range dependencies between pixels, transformer architecture has been adopted to computer ...
Zinan Xiong +4 more
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Sq-Swin: Siamese Quadratic Swin Transformer for Lettuce Browning Prediction
IEEE Access, 2023 Enzymatic browning is a major quality defect of packaged “ready-to-eat” fresh-cut lettuce salads. While there have been many research and breeding efforts to counter this problem, progress is hindered by the lack of a technology to identify and quantify browning rapidly, objectively, and reliably. Here, we report a deep learning model for
Dayang Wang +4 more
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Face-based age estimation using improved Swin Transformer with attention-based convolution
Frontiers in Neuroscience, 2023 Recently Transformer models is new direction in the computer vision field, which is based on self multihead attention mechanism. Compared with the convolutional neural network, this Transformer uses the self-attention mechanism to capture global ...
Chaojun Shi +6 more
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Self-Supervised Learning with Swin Transformers
CoRR, 2021 We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers as its backbone architecture. The approach basically has no new inventions, which is combined from MoCo v2 and BYOL and tuned to achieve reasonably high accuracy on ImageNet ...
Zhenda Xie +6 more
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Classification of Solar Radio Spectrum Based on Swin Transformer
Universe, 2022 Solar radio observation is a method used to study the Sun. It is very important for space weather early warning and solar physics research to automatically classify solar radio spectrums in real time and judge whether there is a solar radio burst. As the
Jian Chen +5 more
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Swin–MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
Applied Sciences, 2023 Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs
Zifan Rong +3 more
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