In the context of image enhancement, maximizing NIMA score as a prior can increase the likelihood of enhancing perceptual quality of an image.

**PolarNet** is an improved grid representation for online, single-scan LiDAR point clouds. Instead of using common spherical or bird's-eye-view projection, the polar bird's-eye-view representation balances the points across grid cells in a polar coordinate system, indirectly aligning a segmentation network's attention with the long-tailed distribution of the points along the radial axis.

PolarNet

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

NIMA

NIMA: Neural Image Assessment

**SimAdapter** is a module for explicitly learning knowledge from adapters. SimAdapter aims to learn the similarities between the source and target languages during fine-tuning using the adapters, and the similarity is based on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1). 

The detailed composition of the SimAdapter is shown in the Figure. By taking the language-agnostic representations from the backbone model as the query, and the language-specific outputs from multiple adapter as the keys and values, the final output for SimAdapter over attention are computed as (For notation simplicity, we omit the layer index $l$ below):

$$
\operatorname{SimAdapter}\left(\mathbf{z}, \mathbf{a}\_{\left$S\_{1}, S\_{2}, \ldots, S\_{N}\right$}\right)=\sum_{i=1}^{N} \operatorname{Attn}\left(\mathbf{z}, \mathbf{a}\_{S\_{i}}\right) \cdot\left(\mathbf{a}\_{S\_{i}} \mathbf{W}\_{V}\right)
$$

where SimAdapter $(\cdot)$ and $\operatorname{Attn}(\cdot)$ denotes the SimAdapter and attention operations, respectively. Specifically, the attention operation is computed as:

$$
\operatorname{Attn}(\mathbf{z}, \mathbf{a})=\operatorname{Softmax}\left(\frac{\left(\mathbf{z} \mathbf{W}\_{Q}\right)\left(\mathbf{a} \mathbf{W}\_{K}\right)^{\top}}{\tau}\right)
$$

where $\tau$ is the temperature coefficient, $\mathbf{W}\_{Q}, \mathbf{W}\_{K}, \mathbf{W}\_{V}$ are attention matrices. Note that while $\mathbf{W}\_{Q}, \mathbf{W}\_{K}$ are initialized randomly, $\mathbf{W}\_{V}$ is initialized with a diagonal of ones and the rest of the matrix with small weights $(1 e-6)$ to retain the adapter representations. Furthermore, a regularization term is introduced to avoid drastic feature changes:

$$
\mathcal{L}\_{\mathrm{reg}}=\sum\_{i, j}\left(\left(\mathbf{I}\_{V}\right)\_{i, j}-\left(\mathbf{W}\_{V}\right)_{i, j}\right)^{2}
$$

where $\mathbf{I}\_{V}$ is the identity matrix with the same size as $\mathbf{W}\_{V}$

Source	NIMA: Neural Image Assessment
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com