Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms

Related tags

Deep Learning LESA
Overview

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.

Method Model Top-1 Acc. Top-5 Acc.
LESA_ResNet50 Download 79.55 94.79
LESA_WRN50 Download 80.18 95.07

Main Results on COCO test-dev

Please refer to LESA_detection for details.

Method Backbone Pretrained Model Box AP Mask AP
Mask-RCNN LESA_ResNet50 Download Download 44.2 39.6
HTC LESA_WRN50 Download Download 50.5 44.4

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.

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Owner
Chenglin Yang
PhD student in computer science
Chenglin Yang
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