Miscellaneous Components
# Positional Encoding Generator

Introduced by Chu et al. in Conditional Positional Encodings for Vision Transformers
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#### Usage Over Time

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Categories

**Positional Encoding Generator**, or **PEG**, is a module used in the Conditional Position Encoding position embeddings. It dynamically produce the positional encodings conditioned on the local neighborhood of an input token. To condition on the local neighbors, we first reshape the flattened input sequence $X \in \mathbb{R}^{B \times N \times C}$ of DeiT back to $X^{\prime} \in \mathbb{R}^{B \times H \times W \times C}$ in the 2 -D image space. Then, a function (denoted by $\mathcal{F}$ in the Figure) is repeatedly applied to the local patch in $X^{\prime}$ to produce the conditional positional encodings $E^{B \times H \times W \times C} .$ PEG can be efficiently implemented with a 2-D convolution with kernel $k(k \geq 3)$ and $\frac{k-1}{2}$ zero paddings. Note that the zero paddings here are important to make the model be aware of the absolute positions, and $\mathcal{F}$ can be of various forms such as separable convolutions and many others.

Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Semantic Segmentation | 2 | 18.18% |

Image Classification | 2 | 18.18% |

Supervised Video Summarization | 1 | 9.09% |

Video Summarization | 1 | 9.09% |

Instance Segmentation | 1 | 9.09% |

Novel View Synthesis | 1 | 9.09% |

Classification | 1 | 9.09% |

General Classification | 1 | 9.09% |

Translation | 1 | 9.09% |