Zobrazeno 1 - 10
of 327
pro vyhledávání: '"Zhang Bingyi"'
Publikováno v:
Redai dili, Vol 43, Iss 11, Pp 2203-2215 (2023)
With rural tourism playing a crucial role in rural revitalization, the transition of villagers' livelihoods in the early stage of tourism development has become the focus of academic circles, and the willingness of villagers to transition their livel
Externí odkaz:
https://doaj.org/article/950c4661317d4bdc9ef61d2599e26476
Publikováno v:
Redai dili, Vol 43, Iss 10, Pp 2012-2023 (2023)
As the owners of tourism resources and influencers of tourism development in a destination, residents' attitudes play a significant role in the development of tourism in the exploration stage. During this first stage in the life cycle of a tourism si
Externí odkaz:
https://doaj.org/article/faefc842b52646b5aa48dca4adbb34cd
Graph neural networks (GNNs) have recently empowered various novel computer vision (CV) tasks. In GNN-based CV tasks, a combination of CNN layers and GNN layers or only GNN layers are employed. This paper introduces GCV-Turbo, a domain-specific accel
Externí odkaz:
http://arxiv.org/abs/2404.07188
Autor:
Wickramasinghe, Sachini, Parikh, Dhruv, Zhang, Bingyi, Kannan, Rajgopal, Prasanna, Viktor, Busart, Carl
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is a key technique used in military applications like remote-sensing image recognition. Vision Transformers (ViTs) are the current state-of-the-art in various computer vision applicati
Externí odkaz:
http://arxiv.org/abs/2404.04527
Vision Transformers (ViTs) have achieved state-of-the-art accuracy on various computer vision tasks. However, their high computational complexity prevents them from being applied to many real-world applications. Weight and token pruning are two well-
Externí odkaz:
http://arxiv.org/abs/2403.14047
Autor:
Fein-Ashley, Jacob, Wickramasinghe, Sachini, Zhang, Bingyi, Kannan, Rajgopal, Prasanna, Viktor
Image classifiers for domain-specific tasks like Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) and chest X-ray classification often rely on convolutional neural networks (CNNs). These networks, while powerful, experience high latenc
Externí odkaz:
http://arxiv.org/abs/2402.00564
Synthetic Aperture Radar (SAR) images are commonly utilized in military applications for automatic target recognition (ATR). Machine learning (ML) methods, such as Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), are frequently us
Externí odkaz:
http://arxiv.org/abs/2401.02687
Graph Neural Networks (GNNs) have gained significant momentum recently due to their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are the current state-of-the-art for point cloud applications; such applications (viz. autonomous
Externí odkaz:
http://arxiv.org/abs/2309.09142
Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by exploiting data sparsity in the input graph, vertex features, and inte
Externí odkaz:
http://arxiv.org/abs/2308.02749
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is the key technique for remote sensing image recognition. The state-of-the-art works exploit the deep convolutional neural networks (CNNs) for SAR ATR, leading to high computation cos
Externí odkaz:
http://arxiv.org/abs/2305.07119