Depthwise Separable Convolution实现了很高程度的轻量化,但是仔细分析,会发现上述假设情况下,Depthwise Convolution的卷积核参数量和乘法计算量都只占Depthwise Separable Convolution的3.4%,Pointwise Convolution占比高达96.6%!因此,Pointwise Convolution是更进一步轻量化模型的关键。 从前文所述中,我们可以看出Pointwise Convol...
Compared to existing point cloud segmentation methods that are commonly based on traditional convolutional neural networks (CNNs), our proposed method is less sensitive to data distribution and computational powers. This framework mainly includes four modules. Module I is first designed to construct a ...
We study the spectral convergence of graph Laplacians to the Laplace-Beltrami operator when the kernelized graph affinity matrix is constructed from N rand... X Cheng,N Wu - 《Applied & Computational Harmonic Analysis》 被引量: 0发表: 2022年 加载更多研究点推荐 convolution type operators heat ke...
Point-wise riskOptimalityAdaptivityWaveletAnisotropic Hölder spaceBy using a kernel method, Lepski and Willer establish adaptive and optimal Lp risk estimations in the convolution structure density model in 2017 and 2019. They assume their density functions to be in a Nikol'skii space. Motivated by...
In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. This paper presents a point-wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic...
The proposed backbone uses point-wise separable (PWS) and depth-wise separable convolutions, which are more efficient than standard convolution. The PWS convolution utilizes a residual shortcut link to reduce computation time. We also propose a SFPN that comprises concatenation, transformer encoder-...
Contextual information aggregation through di- lated convolution is proposed by [4,42]. Dilations are introduced into the clas- sical compact convolution module to expand the receptive field. Contextual in- formation aggregation can also be achieved through pooling operation. Global pooling ...
While some researches have attempted to build a domain specific lexicon from scratch using various methods (Saif et al., 2014), others have sought to adapt existing lexicons for a given domain (Khan et al., 2016). There is performance enhancement in both cases but not enough to deliver ...
Context information plays a key role for image understanding. Dilated convolution [4,42] inserted dilation inside classical convolution kernels to enlarge the receptive field of CNN. Global pooling was widely adopted in various basic classification backbones [13,14,19,35,36] to harvest context infor...
Currently, many graph kernels are defined based on R-convolution theory for construction, but such kernels have three drawbacks: A large amount of structural information in non-isomorphic subgraphs is ignored. The positions of isomorphic sub-structures in the original network cannot be reflected by ...