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Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
[article]
2020
arXiv
pre-print
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Our dilated point convolutions alleviate this issue, they significantly increase the receptive field size of point convolutions. ...
However, we observe that the receptive field size of recent point convolutional networks is inherently limited. ...
Acknowledgements: This work was supported by the ERC Consolidator Grant DeeViSe(ERC-2017-COG-773161). We thank Mats Steinweg, Dan Jia, Jonas Schult and Alexander Hermans for their valuable feedback. ...
arXiv:1907.12046v3
fatcat:57jqxvwdobfxfnx4htwbcvafs4
A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest Recommendation
2020
ISPRS International Journal of Geo-Information
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the user's complicated short- and long-range check-in sequence by using a stack of dilated causal convolution layers and residual block structure. ...
With the growing popularity of location-based social media applications, point-of-interest (POI) recommendation has become important in recent years. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijgi9020113
fatcat:4n67u67rc5dt3p7qcj6rnfzkyy
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders
[article]
2022
arXiv
pre-print
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Due to the unicity of receptive field, semantic segmentation of point clouds remains challenging for the expression of multi-receptive field features, which brings about the misclassification of instances ...
of receptive field bases. ...
Acknowledgments This work is supported in part by National Natural Science Foundation of China (NSFC) under Grant 61725105. ...
arXiv:2204.04944v2
fatcat:3iswkmkgbbfexi4sanoagpqfle
Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring
[article]
2020
arXiv
pre-print
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To solve these problems, we propose a sequence to point learning framework based on bidirectional (non-casual) dilated convolution for NILM. ...
The ability of neural networks to extract load features is closely related to its depth. ...
The authors would like to thank Multi-Function Computer Center of Guangxi University for its high-performance computer. ...
arXiv:2006.00250v1
fatcat:tye5xssxbferpj2yiin52kkjom
DGANet: A Dilated Graph Attention-Based Network for Local Feature Extraction on 3D Point Clouds
2021
Remote Sensing
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Feature extraction on point clouds is an essential task when analyzing and processing point clouds of 3D scenes. ...
Moreover, by integrating the dilated graph attention module (DGAM) implemented by a novel offset–attention mechanism, the proposed network promises to highlight the differing ability of each edge of the ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs13173484
fatcat:ore6i4n2yncc3dmykjnim7cecy
Log-Polar Space Convolution for Convolutional Neural Networks
[article]
2021
arXiv
pre-print
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The local receptive field grows exponentially with the number of distance levels. ...
Since the number of parameters increases quadratically with the size of the convolution kernel, many popular models use small convolution kernels, resulting in small local receptive fields in lower layers ...
Relation with effective receptive field [30] . In [30] , it is found that the effective receptive field only occupies a fraction of the full theoretical receptive field. ...
arXiv:2107.11943v1
fatcat:3xtmjmpitjggbge3p324wefqtu
Deformable Convolutional Networks
[article]
2017
arXiv
pre-print
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Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released. ...
Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. ...
The Aligned-Inception-ResNet model is pre-trained on ImageNet-1K classification [8] . The training procedure follows [22] . ...
arXiv:1703.06211v3
fatcat:uc7zirurcbdplamqneuacphnya
Active Convolution: Learning the Shape of Convolution for Image Classification
[article]
2017
arXiv
pre-print
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The convolution layer is the core of the CNN, but few studies have addressed the convolution unit itself. In this paper, we introduce a convolution unit called the active convolution unit (ACU). ...
Second, the shape of the convolution is learned while training and there is no need to tune it by hand. ...
VGG [21] is based on the idea that a stack of two convolutional layers with a receptive field 3 × 3 is more effective than a 5 × 5 convolution. ...
arXiv:1703.09076v1
fatcat:jxw72xykzfa77alx733aow6lmq
Active Convolution: Learning the Shape of Convolution for Image Classification
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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The convolution layer is the core of the CNN, but few studies have addressed the convolution unit itself. In this paper, we introduce a convolution unit called the active convolution unit (ACU). ...
Second, the shape of the convolution is learned while training and there is no need to tune it by hand. ...
Acknowledgement This work was partly supported by the ICT R&D program of MSIP/IITP, 2016-0-00563, Research on Adaptive Machine Learning Technology Development for Intelligent Autonomous Digital Companion ...
doi:10.1109/cvpr.2017.200
dblp:conf/cvpr/JeonK17
fatcat:emonls2lhrhxrmy5rrmlleoo3a
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
[article]
2019
arXiv
pre-print
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Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. ...
Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art ...
Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing endorsements, either expressed or implied, of IARPA, DOI/IBC, or ...
arXiv:1811.11431v3
fatcat:ylym4agztzfxznnf2sxs5qlrce
Quantum Dilated Convolutional Neural Networks
2022
IEEE Access
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Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks. ...
We perform empirical experiments on MNIST and Fashion-MNIST datasets for the task of image recognition and demonstrate that QDCNN models generally enjoy better performances in terms of both accuracy and ...
It is noteworthy that even though the quantum dilated convolution is able to expand the receptive field the number of data points that are fed into the quantum convolution circuit is the same as the one ...
doi:10.1109/access.2022.3152213
fatcat:y7vupsmyandb7hnm5u4qxd7dze
C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1812.04920v2 fatcat:br4vk5vo55ggxcnzuwnpxpaleiOther Versions
The second stage increases the receptive field by using a depth-wise separable dilated convolution from the feature map of the first stage. ...
One of the practical choices for making a lightweight semantic segmentation model is to combine a depth-wise separable convolution with a dilated convolution. ...
The comprehensive convolution stage uses dilation to see the large receptive field, followed by the point-wise convolution that mixes the channel information. ...
arXiv:1812.04920v3
fatcat:ostkgdbz45e7hjdzpw7qh67hrq
Understanding Convolution for Semantic Segmentation
[article]
2018
arXiv
pre-print
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This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution ...
We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. ...
Acknowledgments We thank the members of TuSimple and Gary's Unbelievable Research Unit (GURU) for comments on this work. ...
arXiv:1702.08502v3
fatcat:cgmtdmjhozechgh3qipjr73egy
Dilated Skip Convolution for Facial Landmark Detection
2019
Sensors
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convolution filters and (2) a dilated skip convolution subnet—a combination of dilated convolutions and skip-connections networks—that are in charge of robustly refining the local appearance heatmaps. ...
Through this proposed architecture, we demonstrate that our approach achieves state-of-the-art performance on challenging datasets—including LFPW, HELEN, 300W and AFLW2000-3D—by leveraging fully convolutional ...
Figure 2 illustrates how dilated convolutions enlarge the receptive fields by altering dilation factors (d). ...
doi:10.3390/s19245350
pmid:31817213
pmcid:PMC6960628
fatcat:gxpwzfeblvgylnazw3ra6vu5iu
Submanifold Sparse Convolutional Networks
[article]
2017
arXiv
pre-print
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"dilating" the observation with every layer in the network. ...
Our empirical analysis of the resulting submanifold sparse convolutional networks shows that they perform on par with state-of-the-art methods whilst requiring substantially less computation. ...
The difference between both operations is in how they handle active sites: instead of automatically making a site active if any of the inputs to its receptive field is active (thereby dilating the set ...
arXiv:1706.01307v1
fatcat:qiiqarroe5gujmxnrhn5vi5hyu
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