Official implementation of the Variational Distillation framework from "Farewell to Mutual Information: Variational Distiilation for Cross-Modal Person Re-identification (CVPR' 21 oral)".

Please read our paper for a more detailed description of the training procedure.

Please use the following bibtex for citations:

@inproceedings{VariationalDistillation,
title={Farewell to Mutual Information Variational Distiilation for Cross-Modal Person Re-identification},
author={Xudong Tian and Zhizhong Zhang and Shaohui Lin and Yanyun Qu and Yuan Xie and Lizhuang Ma},
booktitle={Computer Vision and Pattern Recognition},
year={2021}
}

1. cd to folder where you want to download this project

2. Run git clone https://github.com/FutabaSakuraXD/Farewell-to-Mutual-Information-Variational-Distiilation-for-Cross-Modal-Person-Re-identification.git

3. Install dependencies:

   • python>=3.7.0
   • pytorch>=1.3.0
   • torchvision

4. Prepare datasets

   • Download RegDB
   • Download SYSU-MM01

   Create a directory to store the required datasets under this project or outside this project, and remember to set --data-dir to the right path before training.

This project provides code to train and evaluate different architectures under both datasets. You can directly run /mm01.py and regdb.py under the default settings or conduct customized modifications for both datasets.

• MM01: To evaluate the model under standard protocol, you need to run /feature_extract.py to obtain features at first, then run /evaluation/evaluation_SYSU_MM01.py to conduct standard evaluation.
• RegDB: You can directly run /RegDB_test/RegDB_test.py to obtain Visible-Thermal performance, and change the default settings to evaluate the model under another setting, i.e., Thermal-Visible.

SYSU-MM01 (all-search mode)

SYSU-MM01 (indoor-search mode)

RegDB

2-D projection of the embedding space obtained by using t-SNE. The results are obtained from our method and the conventional information bottleneck on SYSU-MM01 dataset. Different colors are used to denote different person IDs.

In addition, we plot the joint embedding space of data from different modals for better visualization. Note more descriptions and details could be found in our paper.