LIP_SSL

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Code repository for Self-supervised Structure-sensitive Learning, CVPR'17

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## Self-supervised Structure-sensitive Learning (SSL)
Ke Gong, Xiaodan Liang, Xiaohui Shen, Liang Lin, "Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing", CVPR 2017.

### Introduction

SSL is a state-of-the-art deep learning methord for human parsing built on top of [Caffe](http://caffe.berkeleyvision.org).
This novel self-supervised structure-sensitive learning approach can impose human pose structures into parsing results without resorting to extra supervision (i.e., no
need for specifically labeling human joints in model training). The self-supervised learning framework can be injected into any advanced neural networks to help incorporate rich high-level knowledge regarding human joints from a global perspective and improve the parsing results.

This distribution provides a publicly available implementation for the key model ingredients reported in our latest [paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Gong_Look_Into_Person_CVPR_2017_paper.pdf) which is accepted by CVPR2017.

We newly introduce a novel Joint Human Parsing and Pose Estimation Network (**JPPNet**), which is accepted by T-PAMI 2018.   ([Paper](https://arxiv.org/pdf/1804.01984.pdf) and [Code](https://github.com/Engineering-Course/LIP_JPPNet))

Please consult and consider citing the following papers:

@InProceedings{Gong_2017_CVPR,
      author = {Gong, Ke and Liang, Xiaodan and Zhang, Dongyu and Shen, Xiaohui and Lin, Liang},
      title = {Look Into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing},
      booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      month = {July},
      year = {2017}
    }
    @article{liang2018look,
      title={Look into Person: Joint Body Parsing \& Pose Estimation Network and a New Benchmark},
      author={Liang, Xiaodan and Gong, Ke and Shen, Xiaohui and Lin, Liang},
      journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
      year={2018},
      publisher={IEEE}
    }

### Look into People (LIP) Dataset

The SSL is trained and evaluated on our [LIP dataset](https://lip.sysuhcp.com/) for human parsing.  Please check it for more model details. The dataset is also available at [google drive](https://drive.google.com/drive/folders/0BzvH3bSnp3E9QjVYZlhWSjltSWM?resourcekey=0-nkS8bDVjPs3bEw3UZW-omA&usp=sharing) and [baidu drive](http://pan.baidu.com/s/1nvqmZBN).

### Pre-trained models

We have released our trained models with best performance at [google drive](https://drive.google.com/open?id=0BzvH3bSnp3E9eHMyVS1RbUVDems) and [baidu drive](http://pan.baidu.com/s/1dFLCYq9).

### Train and test

1. Download LIP dataset or prepare your own data.
2. Put the images(*.jpg) and segmentations(*.png) into ssl/human/data/images and ssl/human/data/labels
3. Put the train, val, test lists into ssl/human/list. Each type contains a list for path and a list for id (e.g., train.txt and train_id.txt) 
4. Download the pre-trained model and put it into ssl/human/model/attention/. You can also refer [DeepLab](https://bitbucket.org/aquariusjay/deeplab-public-ver2) for more models. 
5. Set up your init.caffemodel before training and test.caffemodel before testing. You can simply use a soft link.
6. The prototxt files for network config are saved in ssl/human/config
7. In run_human.sh, you can set the value of RUN_TRAIN adn RUN_TEST to train or test the model.
8. After you run TEST, the computed features will be saved in ssl/human/features. You can run the provided MATLAB script, show.m to generate visualizable results. Then you can run the Python script, test_human.py to evaluate the performance.

## Related work
+ Joint Body Parsing & Pose Estimation Network [JPPNet](https://github.com/Engineering-Course/LIP_JPPNet)， T-PAMI2018
+ Instance-level Human Parsing via Part Grouping Network [PGN](https://github.com/Engineering-Course/CIHP_PGN), ECCV2018
+ Graphonomy: Universal Human Parsing via Graph Transfer Learning [Graphonomy](https://github.com/Gaoyiminggithub/Graphonomy), CVPR2019

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