Abstract

In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at https://github.com/implus/SKNet.

Keywords

Receptive fieldComputer scienceSoftmax functionArtificial intelligencePattern recognition (psychology)Convolutional neural networkKernel (algebra)Artificial neural networkMathematics

Affiliated Institutions

Related Publications

Network In Network

Abstract: We propose a novel deep network structure called In Network (NIN) to enhance model discriminability for local patches within the receptive field. The conventional con...

2014 arXiv (Cornell University) 1037 citations

Squeeze-and-Excitation Networks

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial a...

2019 IEEE Transactions on Pattern Analysis... 12023 citations

Publication Info

Year
2019
Type
article
Pages
510-519
Citations
2769
Access
Closed

External Links

Social Impact

Altmetric

Social media, news, blog, policy document mentions

Citation Metrics

2769
OpenAlex

Cite This

Xiang Li, Wenhai Wang, Xiaolin Hu et al. (2019). Selective Kernel Networks. , 510-519. https://doi.org/10.1109/cvpr.2019.00060

Identifiers

DOI
10.1109/cvpr.2019.00060