Results 31 to 40 of about 206,029 (232)
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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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
doaj +1 more source
Facial Expression Recognition with Swin Transformer
CoRR, 2022 The task of recognizing human facial expressions plays a vital role in various human-related systems, including health care and medical fields. With the recent success of deep learning and the accessibility of a large amount of annotated data, facial expression recognition research has been mature enough to be utilized in real-world scenarios with ...
Jun-Hwa Kim, Namho Kim, Chee Sun Won
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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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Class-guided Swin Transformer for Semantic Segmentation of Remote Sensing Imagery [PDF]
, 2022 Semantic segmentation of remote sensing images plays a crucial role in a wide variety of practical applications, including land cover mapping, environmental protection, and economic assessment.
Wang, Libo +5 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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Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture
, 2023 In this study, we introduce the U-Swin fusion model, an effective and efficient transformer-based architecture designed for the fusion of multi-focus microscope images.
Kun Gao +4 more
core +1 more source
Swin on Axes: Extending Swin Transformers to Quadtree Image Representations [PDF]
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)In recent years, Transformer models have revolutionized machine learning. While this has resulted in impressive re-sults in the field of Natural Language Processing, Computer Vision quickly stumbled upon computation and memory problems due to the high resolution and dimensionality of the input data. This is particularly true for video, where the number
Marc Oliu +3 more
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SwinIR: Image Restoration Using Swin Transformer [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on ...
Jingyun Liang +5 more
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A Swin Transformer-based model for mosquito species identification
Scientific Reports, 2022 Mosquito transmit numbers of parasites and pathogens resulting in fatal diseases. Species identification is a prerequisite for effective mosquito control. Existing morphological and molecular classification methods have evitable disadvantages.
De-zhong Zhao +8 more
doaj +1 more source