Results 11 to 20 of about 1,031,506 (314)
Node-Feature Convolution for Graph Convolutional Networks [PDF]
Pattern Recognition, 2022 Graph convolutional network (GCN) is an effective neural network model for graph representation learning. However, standard GCN suffers from three main limitations: (1) most real-world graphs have no regular connectivity and node degrees can range from one to hundreds or thousands, (2) neighboring nodes are aggregated with fixed weights, and (3) node ...
Zhang, L., Song, H., Aletras, N., Lu, H.
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit.
Liang-Chieh Chen +4 more
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Grid Graph Reduction for Efficient Shortest Pathfinding
IEEE Access, 2023 Single-pair shortest pathfinding (SP) algorithms are used to identify the path with the minimum cost between two vertices in a given graph. However, their time complexity can rapidly increase as the graph size grows.
Chan-Young Kim, Sanghoon Sull
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KPConv: Flexible and Deformable Convolution for Point Clouds [PDF]
IEEE International Conference on Computer Vision, 2019 We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to
Hugues Thomas +5 more
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ECO: Efficient Convolution Operators for Tracking [PDF]
Computer Vision and Pattern Recognition, 2016 In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have
Martin Danelljan +3 more
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Free-Form Image Inpainting With Gated Convolution [PDF]
IEEE International Conference on Computer Vision, 2018 We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the
Jiahui Yu +5 more
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Incorporating Convolution Designs into Visual Transformers [PDF]
IEEE International Conference on Computer Vision, 2021 Motivated by the success of Transformers in natural language processing (NLP) tasks, there emerge some attempts (e.g., ViT and DeiT) to apply Transformers to the vision domain.
Kun Yuan +5 more
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Generalized Quantum Convolution for Multidimensional Data
Entropy, 2023 The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning.
Mingyoung Jeng +8 more
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On the Integration of Self-Attention and Convolution [PDF]
Computer Vision and Pattern Recognition, 2021 Convolution and self-attention are two powerful techniques for representation learning, and they are usually considered as two peer approaches that are distinct from each other.
Xuran Pan +6 more
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DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution [PDF]
Computer Vision and Pattern Recognition, 2020 Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature
Siyuan Qiao, Liang-Chieh Chen, A. Yuille
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