DFN are dynamic and sample-specific. *Deformable Convolutional Networks (DCNs)* [@dai2017deformable]. DCNs introduce deformable transformation into convolution. The key idea is to predict offsets for convolutional kernels. With offsets, convolution can be executed on irregular sampling grids, enabling adaptive manipulation of the receptive field.

*Attention Networks* [@mnih2014recurrent]. Attention networks are a broad family of networks that use attention mechanisms. The mechanisms introduce multiplicative interactions between the inferred attention map and the feature map. In computer vision, attention mechanisms are usually referred to spatial attention [@wang2017residual], channel attention [@hu2018squeeze] or both [@woo2018cbam]. These network modules are widely applied in CNNs to force the network focusing on specific regions and therefore to refine feature maps. Essentially, attention is about feature selection. No attentional module has been designed to deal with the downsampling/upsampling stage.

In contrast to above dynamic networks, IndexNet focuses on upsampling, rather than manipulating filters or refining features. Akin to dynamic networks above, the dynamics in IndexNet also has a physical definition—indices. Such a definition also closely relates to attention networks. Later we show that the downsampling and upsampling operators used with IndexNet can, to some extent, be viewed as attentional operators. Indeed, max-pooling indices are a form of hard attention. 