Results 81 to 90 of about 14,679 (231)
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer
, 2023 Image captured under low-light conditions presents unpleasing artifacts, which debilitate the performance of feature extraction for many upstream visual tasks.
Gao, Yanzeng +4 more
core
Self-Supervised Learning with Swin Transformers
, 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 ...
Xie, Zhenda +6 more
openaire +2 more sources
IET Power Electronics, EarlyView.The multisource adaptive fusion CNN‐transformer method proposed in this study can provide theoretical and technical support for fault diagnosis of high‐voltage MCHBI. It has a promising potential for engineering applications. Abstract In high‐voltage applications, the number of cascaded H‐bridge inverter units is large, the failure probability ...
Weiman Yang +4 more
wiley +1 more source
PVTv2: Improved Baselines with Pyramid Vision Transformer
, 2021 Transformer recently has shown encouraging progresses in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (abbreviated as PVTv1) by adding three designs, including (1) overlapping patch ...
Fan, Deng-Ping +8 more
core +1 more source
Cladistics, EarlyView.Abstract Automated insect identification systems hold significant value for biodiversity monitoring, pest management, citizen science initiatives and systematic studies, particularly in an era of declining expertise in insect taxonomy. However, current deep learning approaches often rely on standardized specimen photos from limited‐angles and ...
Xinkai Wang +10 more
wiley +1 more source
Remote SensingTransformer-based deep learning techniques have recently shown outstanding potential in remote sensing scene classification (RSSC), benefiting from their ability to capture global semantic relationships and contextual dependencies.
Xiaozhang Zhu +2 more
doaj +1 more source
Journal of Microscopy, EarlyView.Abstract X‐ray phase contrast imaging (XPCI), when implemented in micro‐computed tomography (micro‐CT) mode, offers high‐contrast 3D imaging of weakly‐attenuating material samples. In the so‐called single‐mask edge illumination approach, a mask with periodically spaced transmitting apertures is used to split the x‐ray beam into narrow beamlets; when ...
Khushal Shah +8 more
wiley +1 more source
Expert Systems, Volume 43, Issue 3, March 2026.ABSTRACT Modelling the evolution of Alzheimer's disease (AD) requires a thorough spatiotemporal study of longitudinal neuroimaging data. We propose in this paper a novel deep learning framework that uses a parallel combination of Recurrent Neural Networks (RNNs) and Vision Transformers (ViT) to extract temporal disease dynamics and spatial structural ...
Sahbi Bahroun, Gwanggil Jeon
wiley +1 more source
Leadership for innovation – why manufacturing has a future in Australia [PDF]
In this paper, business leaders discuss the leadership styles they have used to ensure their companies are manufacturing success stories, and then these experiences are analysed to outline the leadership needs for innovation in Australia.
Richard Simpson +2 more
core
Precipitation Nowcasting With Spatial And Temporal Transfer Learning Using Swin-UNETR
, 2023 Climate change has led to an increase in frequency of extreme weather events. Early warning systems can prevent disasters and loss of life. Managing such events remain a challenge for both public and private institutions.
Kumar, Ajitabh
core