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listwise-view-ranking

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# Listwise-View-Ranking-Image-Cropping

### Material
[Paper](https://arxiv.org/pdf/1905.05352.pdf), [Model(Google)](https://drive.google.com/open?id=1WVUsvR3MHhApCapyXUBIyslwG2yVOnxK), [Model(Baidu)](https://pan.baidu.com/s/1mLP2pzW3IUEPpVs13l579Q)

### Citation
```
@article{lu2019listwise,
  title={Listwise View Ranking for Image Cropping},
  author={Lu, Weirui and Xing, Xiaofen and Cai, Bolun and Xu, Xiangmin},
  journal={arXiv preprint arXiv:1905.05352},
  year={2019}
}
```

### Abstract
Rank-based Learning with deep neural network has been widely used for image cropping. However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning. In this paper, we develop a novel model to overcome these problems. To address the first problem, we formulate the image cropping as a listwise ranking problem to find the best view composition. For the second problem, a refined view sampling (called RoIRefine) is proposed to extract refined feature maps for candidate view generation. Given a series of candidate views, the proposed model learns the Top-1 probability distribution of views and picks up the best one. By integrating refined sampling and listwise ranking, the proposed network called LVRN achieves the state-of-the-art performance both in accuracy and speed.

### Prerequisites
Pytorch 0.4.1

### Run demo
1. Put your test images into ``images`` folder.
2. Download the pre-trained model into ``model`` folder. 
3. ``cd lib`` and run ``make.sh`` to build roi_crop, roi_align and roi_pooling modules.
4. run ``python demo.py --GPU x``

### Train
1. Download the train dataset(CPC), ``cd lib/utils `` and modify ``config.py``
2. run ``python generatePdefinedAnchors.py`` to generate pre-defined anchors for training and evaluation.
3. run ``python createImdbDataset.py`` to create lmdb-type datasets for training.
4. ``cd .. `` and run ``make.sh`` to build roi_crop, roi_align and roi_pooling modules.
5. run ``python train.py --GPU x --bs x --lr x``

### Evaluation
1. Download the cropping dataset(FCDB and FLMS), ``cd lib/utils `` and modify ``config.py``.
2. Download the pre-trained model into ``model`` folder. 
3. ``cd lib`` and run ``make.sh`` to build roi_crop, roi_align and roi_pooling modules.
4. modify the path of ``create_gt_crops.py`` and run ``python create_gt_crops.py`` to create the ground-truth.
5. run ``python demo.py --GPU x``

### Qualitative visualization on FCDB dataset
<img src="https://github.com/luwr1022/listwise-view-ranking/blob/master/examples/lvrn.jpg" alt="gt" align=center />

### .
In this work, the RoI operation (RoIPool, RoIAlign and RoIRefine) are based on [https://github.com/jwyang/faster-rcnn.pytorch](url).

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