Decoupled Dynamic Filter Networks
Jingkai Zhou, Varun Jampani, Zhixiong Pi, Qiong Liu, Ming-Hsuan Yang

TL;DR
This paper introduces Decoupled Dynamic Filter (DDF), a novel convolution method that reduces computational costs and enhances performance by decoupling spatial and channel filters, outperforming standard and depth-wise convolutions.
Contribution
The paper proposes DDF, a decoupled dynamic filter that improves CNN efficiency and accuracy by separating spatial and channel filtering, inspired by attention mechanisms.
Findings
ResNet50/101 accuracy improved by 1.9%/1.3%
Computational costs nearly halved
DDF outperforms standard and content-adaptive layers in detection and upsampling tasks
Abstract
Convolution is one of the basic building blocks of CNN architectures. Despite its common use, standard convolution has two main shortcomings: Content-agnostic and Computation-heavy. Dynamic filters are content-adaptive, while further increasing the computational overhead. Depth-wise convolution is a lightweight variant, but it usually leads to a drop in CNN performance or requires a larger number of channels. In this work, we propose the Decoupled Dynamic Filter (DDF) that can simultaneously tackle both of these shortcomings. Inspired by recent advances in attention, DDF decouples a depth-wise dynamic filter into spatial and channel dynamic filters. This decomposition considerably reduces the number of parameters and limits computational costs to the same level as depth-wise convolution. Meanwhile, we observe a significant boost in performance when replacing standard convolution with…
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Taxonomy
TopicsHuman Pose and Action Recognition · Advanced Neural Network Applications · Video Surveillance and Tracking Methods
MethodsConvolution
