Section \[ssec:comparison\] and in experiments.

**Dynamic Networks.** If considering the dynamic property of IndexNet, [IndexNet]{} shares similarity with an interesting group of networks—dynamic networks. Dynamic networks are often implemented with adaptive modules to extend the modeling capabilities of CNNs. These networks share the following characteristics. The output is *dynamic*, conditioned on the input feature map. Since dynamic networks are learnable modules, they are *generic* in the sense that they can be used as building blocks in many network architectures. They are also *flexible* to allow modifications according to target tasks. *Spatial Transformer Networks (STNs)* [@jaderberg2015spatial]. STN allows explicit manipulation of spatial transformation within the network. It achieves this by regressing transformation parameters $\theta$ with a side-branch network. A spatially-transformed output is then produced by a sampler parameterized by $\theta$. This results in a holistic transformation of the feature map. The dynamic nature of STN is reflected by the fact that, given different inputs, the inferred $\theta$ is different, allowing to learn some forms of invariance to translation, scale, rotation, etc. *Dynamic Filter Networks (DFNs)* [@jia2016dynamic]. DFN implements a filter generating network to dynamically generate kernel filter parameters Compared to conventional filter parameters that stay fixed during inference, filter parameters in 