Awesome

Efficient Attention

An implementation of the efficient attention module.

Description

Efficient attention is an attention mechanism that substantially optimizes the memory and computational efficiency while retaining exactly the same expressive power as the conventional dot-product attention. The illustration above compares the two types of attention. The efficient attention module is a drop-in replacement for the non-local module (Wang et al., 2018), while it:

• uses less resources to achieve the same accuracy;
• achieves higher accuracy with the same resource constraints (by allowing more insertions); and
• is applicable in domains and models where the non-local module is not (due to resource constraints).

Resources

YouTube:

• Presentation: https://youtu.be/_wnjhTM04NM

bilibili (for users in Mainland China):

• Presentation: https://www.bilibili.com/video/BV1tK4y1f7Rm
• Presentation in Chinese: https://www.bilibili.com/video/bv1Gt4y1Y7E3

Implementation details

This repository implements the efficient attention module with softmax normalization, output reprojection, and residual connection.

Features not in the paper

This repository implements additionally implements the multi-head mechanism which was not in the paper. To learn more about the mechanism, refer to Vaswani et al.

Citation

The paper will appear at WACV 2021. If you use, compare with, or refer to this work, please cite

@inproceedings{shen2021efficient,
    author = {Zhuoran Shen and Mingyuan Zhang and Haiyu Zhao and Shuai Yi and Hongsheng Li},
    title = {Efficient Attention: Attention with Linear Complexities},
    booktitle = {WACV},
    year = {2021},
}