Results 271 to 280 of about 1,031,506 (314)
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Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
European Conference on Computer Vision, 2016Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking.
Martin Danelljan +3 more
semanticscholar +1 more source
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction
IEEE transactions on intelligent transportation systems (Print), 2021Traffic prediction is a core problem in the intelligent transportation system and has broad applications in the transportation management and planning, and the main challenge of this field is how to efficiently explore the spatial and temporal ...
Kan Guo +7 more
semanticscholar +1 more source
Coupled convolution layer for convolutional neural network
Neural Networks, 2016We propose a coupled convolution layer comprising multiple parallel convolutions with mutually constrained filters. Inspired by biological human vision mechanism, we constrain the convolution filters such that one set of filter weights should be geometrically rotated, mirrored, or be the negative of the other.
Kazutaka Uchida +2 more
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SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
European Conference on Computer Vision, 2020LiDAR point-cloud segmentation is an important problem for many applications. For large-scale point cloud segmentation, the \textit{de facto} method is to project a 3D point cloud to get a 2D LiDAR image and use convolutions to process it.
Chenfeng Xu +6 more
semanticscholar +1 more source
Convolution and Trimming via Convolution
2012What is Trimming? It is identical to convolution in results. Then what are differences? Convolution occurs in a technical system as a phenomenon; it is part of TS evolutionary wave. Trimming is a methodology purposefully applied to a TS to increase its ideality by achieving specific gain in MUF and/or to decrease one or all of M, D, or E from MDE by a ...
Saurabh Kwatra, Yuri Salamatov
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MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
ACM Multimedia, 2019Personalized recommendation plays a central role in many online content sharing platforms. To provide quality micro-video recommendation service, it is of crucial importance to consider the interactions between users and items (i.e. micro-videos) as well
Yin-wei Wei +5 more
semanticscholar +1 more source
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018
Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated
Lichen Zhou, Chuang Zhang, Ming Wu
semanticscholar +1 more source
Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated
Lichen Zhou, Chuang Zhang, Ming Wu
semanticscholar +1 more source
UniFormer: Unifying Convolution and Self-Attention for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Kunchang Li, Yali Wang, Hongsheng Li
exaly
ConvUNeXt: An efficient convolution neural network for medical image segmentation
Knowledge-Based Systems, 2022Gai-Ge Wang, Muwei Jian
exaly
AGConv: Adaptive Graph Convolution on 3D Point Clouds
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Mingqiang Wei, Zeyong Wei, Haoran Zhou
exaly