Person Search with Natural Language Description

This project aims at searching person using natural language description. Mainly based on our CVPR 2017 paper Person Search with Natural Language Description. The code is modified from the Neuraltalk2 written by Andrej Karpathy.

Installation

This code is written in Lua and requires Torch. See the Torch installation documentation for more details. To run this code, the following packages must be installed:

• hdf5 (and the torch-hdf5 package)
• cudnn
• cjson
• loadcaffe

Data Preparation

1. Request the dataset from tong.xiao.work[at]gmail.com (academic only).
   Due to licensing issues, please send us your request using your university email.

2. Save data into a json file that contains a list of image paths, person ID, and raw captions for each image, of the form:

[{"split": "train", "captions": ["A woman is wearing a gray shirt, a pair of brown pants and a pair of shoes.", "She is wearing a dark grey top and light colored pants."], "file_path": "train_query/p11600_s14885.jpg", "id": 11003}]

You can also directly download the json file and save it to "data/reid_raw.json"

3. Run prepro_data.py as follows:

   python prepro_data.py --input_json data/reid_raw.json --images_root data/imgs --max_length 50 --word_count_threshold 2 --output_json data/reidtalk.json --output_h5 data/reidtalk.h5
   

Testing

1. Download the pretrained model from snapshot.

2. Run Retrieval.lua. The result should be around:

   top-1 = 19.71%
   top-5 = 43.20%
   top-10 = 55.24%

Training

1. Download the VGG-16 network pre-trained model from model.

2. Run train.lua.

Citation

@article{li2017person,
  title={Person search with natural language description},
  author={Li, Shuang and Xiao, Tong and Li, Hongsheng and Zhou, Bolei and Yue, Dayu and Wang, Xiaogang},
  journal={arXiv preprint arXiv:1702.05729},
  year={2017}
}