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...
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...
Dilations are introduced into the classi- cal compact convolution module to expand the receptive field. Contextual infor- mation aggregation can also be achieved through pooling operation. Global pool- ing module in ParseNet [24], different-dilation based atrous spatial pyramid pool- ing (ASPP) ...
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 m...
This is the official implementation of our paper SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition Note: Our approch is MLP-based and GCN-free. The graph folder is adopted for different modality. Abstract Graph convolution networks (GCNs) have...
This is the official implementation of our paper SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition Note: Our approch is MLP-based and GCN-free. The graph folder is adopted for different modality. Abstract Graph convolution networks (GCNs) have...
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...
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-...
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 ...
𝑓𝑑𝑡∈ℝ𝑚×𝑛×𝑐ftd∈ℝm×n×c are processed through the convolutional operation, and then 𝑓𝑑𝑡ftd can be seen as a convolution kernel, which slides on 𝑓𝑑𝑠fsd to obtain the score map 𝑠∈ℝ(ℎ−𝑚+1)×(𝑤−𝑛+1)s∈ℝ(h−m+1)×(w...