# PWA

**Repository Path**: zhangcaocao/PWA

## Basic Information

- **Project Name**: PWA
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-06
- **Last Updated**: 2021-06-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Part-based Weighting Aggregation (PWA)
=
Code for our AAAI2018 paper：
-
</br>Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval. [(paper)](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16137) </br>
</br>Jian Xu, Cunzhao Shi, Chengzuo Qi, Chunheng Wang*, Baihua Xiao </br>
>@inproceedings{ PWA, </br>
>author = {Jian Xu and Cunzhao Shi and Chengzuo Qi and Chunheng Wang and Baihua Xiao}, </br>
>title = {Unsupervised Part-Based Weighting Aggregation of Deep Convolutional Features for Image Retrieval}, </br>
>conference = {AAAI Conference on Artificial Intelligence}, </br>
>year = {2018} </br>
>}

NOTE:
-
tools:
</br>1.The python code is based on the python data science platform Anaconda2.
</br>2.The python code is tested on Windows by PyCharm.


data:
</br>3.The features of convolutional layer(Pool5 layer) of VGG16 for Oxford5k and Paris6k datasets are in path "data\feature". 
</br>4.The order of part detectors are in path "data\filter_select".
</br>5.The groundtruth for Oxford5k and Paris6k datasets are in path "data\gt_files".


code:
</br>6.Run evaluate.py, the mAP is printed.
</br>7.Run select_filter.py to get the order of part detectors according to variances.