design.

Since the holistic index map is expandable, the decoder feature map does not need to forcibly increase/reduce its dimensionality to fit the shape of the index map during upsampling. This gives much flexibility for decoder design, while it is not the case for DINs.

The number of parameters in Modelwise O2O DINs and Shared Stagewise O2O DINs is independent of the dimensionality of feature maps.

No matter how large the model capacity is or how wide the feature channels are, the number of parameters in Modelwise O2O DINs remains at a constant level, and that in Shared Stagewise O2O DINs is only proportional to the number of downsampling/upsampling stages. This is desirable as the number of parameters introduced by IndexNet is not significant. However, these two types of IndexNet may be limited to capture sophisticated local patterns.

M2O DINs have the most powerful modeling capability among IndexNet variants, but also introduce many extra parameters.

M2O DINs have higher capacity than HINs and O2O DINs due to the use of standard convolution. Another desirable property of IndexNet is that they may be able to predict the indices from a large local feature map, e.g., $l(\mathcal{X}): \mathbb{R}^{2k\times 2k\times C}\rightarrow\mathbb{R}^{k\times k\times1}$. An 