LESA

Introduction

This repository contains the official implementation of Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms. The code for image classification and object detection is based on axial-deeplab and mmdetection.

Citing LESA

If you find LESA is helpful in your project, please consider citing our paper.

@article{yang2021locally,
  title={Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms},
  author={Yang, Chenglin and Qiao, Siyuan and Kortylewski, Adam and Yuille, Alan},
  journal={arXiv preprint arXiv:2107.05637},
  year={2021}
}

Main Results on ImageNet

Please refer to LESA_classification for details.

Main Results on COCO test-dev

Please refer to LESA_detection for details.

Credits

This project is based on axial-deeplab and mmdetection.

Relative position embedding is based on bottleneck-transformer-pytorch

ResNet is based on pytorch/vision. Classification helper functions are based on pytorch-classification.