**Sample Redistribution** is a [data augmentation](https://paperswithcode.com/methods/category/image-data-augmentation) technique for face detection which augments training samples based on the statistics of benchmark datasets via large-scale cropping. During training data augmentation, square patches are cropped from the original images with a random size from the set $[0.3,1.0]$ of the short edge of the original images. To generate more positive samples for stride 8, the random size range is enlarged from $[0.3,1.0]$ to $[0.3,2.0]$. When the crop box is beyond the original image, average RGB values fill the missing pixels.

The motivation is that for efficient [face detection](https://paperswithcode.com/task/face-detection) under a fixed VGA resolution (i.e. 640×480), most of the faces (78.93%) in [WIDER FACE](https://paperswithcode.com/dataset/wider-face-1) are smaller than 32×32 pixels, and thus they are predicted by shallow stages. To obtain more training samples for these shallow stages, Sample Redistribution (SR) is used.

**Embedded Dot Product Affinity** is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a dot product function in an embedding space:

$$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = \theta\left(\mathbb{x\_{i}}\right)^{T}\phi\left(\mathbb{x\_{j}}\right) $$

Here $\theta\left(x\_{i}\right) = W\_{θ}x\_{i}$ and $\phi\left(x\_{j}\right) = W\_{φ}x\_{j}$ are two embeddings.

The main difference between the dot product and [embedded Gaussian affinity](https://paperswithcode.com/method/embedded-gaussian-affinity) functions is the presence of [softmax](https://paperswithcode.com/method/softmax), which plays the role of an activation function.

Embedded Dot Product Affinity

Non-local Neural Networks

Sample Redistribution

Sample and Computation Redistribution for Efficient Face Detection

PolyConv learns continuous distributions as the convolutional filters to share the weights across different vertices of graphs or points of point clouds.

Source	Sample and Computation Redistribution for Efficient Face Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com