**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel. In the Figure to the right, $F$ represents a [convolution](https://paperswithcode.com/method/convolution) layer and $\otimes$ indicates concatenation.

**Corner Pooling** is a pooling technique for object detection that seeks to better localize corners by encoding explicit prior knowledge. Suppose we want to determine if a pixel at location $\left(i, j\right)$ is a top-left corner. Let $f\_{t}$ and $f\_{l}$ be the feature maps that are the inputs to the top-left corner pooling layer, and let $f\_{t\_{ij}}$ and $f\_{l\_{ij}}$ be the vectors at location $\left(i, j\right)$ in $f\_{t}$ and $f\_{l}$ respectively. With $H \times W$ feature maps, the corner pooling layer first max-pools all feature vectors between $\left(i, j\right)$ and $\left(i, H\right)$ in $f\_{t}$ to a feature vector $t\_{ij}$ , and max-pools all feature vectors between $\left(i, j\right)$ and $\left(W, j\right)$ in $f\_{l}$ to a feature vector $l\_{ij}$. Finally, it adds $t\_{ij}$ and $l\_{ij}$ together.

Corner Pooling

CornerNet: Detecting Objects as Paired Keypoints

VoVNet

An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

**Aging Evolution**, or **Regularized Evolution**, is an evolutionary algorithm for [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). Whereas in tournament selection, the best architectures are kept, in aging evolution we associate each genotype with an age, and bias the tournament selection to choose
the younger genotypes. In the context of architecture search, aging evolution allows us to explore the search space more, instead of zooming in on good models too early, as non-aging evolution would.

Source	An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com