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Implicit Visual-Textual (IVT) - Pytorch

This repository is the implementation of the paper "See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval."

Installation

  1. Creating conda environment
conda create -n ivt python=3.7
conda activate ivt
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=10.2 -c pytorch
  1. Install others
git clone https://github.com/TencentYoutuResearch/PersonRetrieval-IVT.git
cd PersonRetrieval-IVT
pip install -r requirements.txt

Getting Started

Pretrain

You can use our pre-trained model[zxvu] directly, otherwise, you need to download several datasets: Conceptual Captions, SBU Captions, COCO, and Visual Genome

Change the data root in pretrain_cuhk.py, then run:

python train_pretrain.py

Training with text-based re-ID datasets

# We leverage four V-100 GPUs for training on CUHK-PEDES and ICFG-PEDES datasets.
# training with multi-gpus
sh start.sh

# or, you could also train them with a single gpu, but slow speed, maybe better performance.
python train_cuhkpedes_gpu.py
python train_icfg_gpu.py

# As RSTPReid is small, we leverage only one V-100 GPU for training.
python train_rstp.py

Trained Models

We provide our trained models at Baidu Pan[bpvu].

Text-based re-ID Datasets

You can obtain the datasets from corresponding authors. We provide our processed json files at Baidu Pan[xktc].

Citations

If you find our work helpful, please cite using this BibTeX:

@inproceedings{shu2023see,
  title={See finer, see more: Implicit modality alignment for text-based person retrieval},
  author={Shu, Xiujun and Wen, Wei and Wu, Haoqian and Chen, Keyu and Song, Yiran and Qiao, Ruizhi and Ren, Bo and Wang, Xiao},
  booktitle={ Proceedings of the European conference on computer vision Workshops (ECCVW)},
  pages={624--641},
  year={2023},
  organization={Springer}
}

Contact us

If you have any questions, comments or suggestions, please do not hesitate to contact us at shuxj@mail.ioa.ac.cn.