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Referring Expression Object Segmentation with Caption-Aware Consistency, BMVC 2019

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# Referring Expression Object Segmentation with Caption-Aware Consistency

PyTorch implementation of our method for segmenting the object in an image specified by a natural language description.

Contact: Yi-Wen Chen (chenyiwena at gmail dot com)

## Paper

[Referring Expression Object Segmentation with Caption-Aware Consistency](https://arxiv.org/abs/1910.04748) 
[Yi-Wen Chen](https://wenz116.github.io/), [Yi-Hsuan Tsai](https://sites.google.com/site/yihsuantsai/home), [Tiantian Wang](https://tiantianwang.github.io), [Yen-Yu Lin](https://www.citi.sinica.edu.tw/pages/yylin/) and [Ming-Hsuan Yang](http://faculty.ucmerced.edu/mhyang/index.html) 
*British Machine Vision Conference (BMVC)*, 2019

Please cite our paper if you find it useful for your research.

```
@inproceedings{Chen_lang2seg_2019,
  author = {Yi-Wen Chen and Yi-Hsuan Tsai and Tiantian Wang and Yen-Yu Lin and Ming-Hsuan Yang},
  booktitle = {British Machine Vision Conference (BMVC)},
  title = {Referring Expression Object Segmentation with Caption-Aware Consistency},
  year = {2019}
}
```

## Prerequisites
* Python 2.7
* Pytorch 0.2 or 0.3
* CUDA 8.0
* Mask R-CNN: Follow the instructions of the [mask-faster-rcnn](https://github.com/lichengunc/mask-faster-rcnn) repo, preparing everything needed for `pyutils/mask-faster-rcnn`.
* REFER API and data: Use the download links of [REFER](https://github.com/lichengunc/refer) and go to the foloder running `make`. Follow `data/README.md` to prepare images and refcoco/refcoco+/refcocog annotations.
* COCO training set should be downloaded in `pyutils/mask-faster-rcnn/data/coco/images/train2014`.

## Preprocessing
The processed data is uploaded in `cache/prepro/`.

## Training

* `<DATASET> <SPLITBY>` pairs contain: refcoco unc/refcoco+ unc/refcocog umd/refcocog google

* Output model will be saved at `<DATASET>_<SPLITBY>/output_<OUTPUT_POSTFIX>`. If there are trained models in this directory, the model of the latest iteratioin will be loaded.

* The iteration when learning rate decay is specified as `STEPSIZE` in `train_*.sh`.

1. Train the baseline segmentation model with only 1 dynamic filter:
```
./experiments/scripts/train_baseline.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX>
```

2. Train the model with spatial dynamic filters:
```
./experiments/scripts/train_spatial.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX>
```

3. Train the model with spatial dynamic filters and caption loss:
```
./experiments/scripts/train_cycle.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX> att2in2 <CAPTION_LOSS_WEIGHT>
```
The pretrained Mask R-CNN model should be placed at `<DATASET>_<SPLITBY>/output_<OUTPUT_POSTFIX>`. If there are multiple models in the directory, the model of the latest iteration will be loaded.

The pretrained caption model should be placed at `<DATASET>_<SPLITBY>/caption_log_res5_2/`, named as `model-best.pth` and `infos-best.pkl`.

4. Train the model with spatial dynamic filters and response loss:
```
./experiments/scripts/train_response.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX>
```

5. Train the model with spatial dynamic filters, response loss and caption loss:
```
./experiments/scripts/train_cycle_response.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX> att2in2 <CAPTION_LOSS_WEIGHT>
```
The pretrained Mask R-CNN model should be placed at `<DATASET>_<SPLITBY>/output_<OUTPUT_POSTFIX>`. If there are multiple models in the directory, the model of the latest iteration will be loaded.

The pretrained caption model should be placed at `<DATASET>_<SPLITBY>/caption_log_response/`, named as `model-best.pth` and `infos-best.pkl`.

6. Train the model with spatial dynamic filters and response loss for VGG16 and Faster R-CNN:

Download the pre-trained Faster R-CNN model [here](https://drive.google.com/drive/folders/0B7fNdx_jAqhtU1laZEhlM09fazA) (coco_900k-1190k.tar), and put the .pth and .pkl files in `pyutils/mask-faster-rcnn/output/vgg16/coco_2014_train+coco_2014_valminusminival/`
```
./experiments/scripts/train_vgg.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX>
```

## Evaluation
1. Evaluate the baseline segmentation model:
```
./experiments/scripts/eval_baseline.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX> <MODEL_ITER>
```
Evaluate the model at `<DATASET>_<SPLITBY>/output_<OUTPUT_POSTFIX>`, of trained iteration `<MODEL_ITER>`.

Detection and segmentation results will be saved at `experiments/det_results.txt` and `experiments/mask_results.txt` respectively.

2. Evaluate the model with spatial dynamic filters (and caption loss):
```
./experiments/scripts/eval_spatial.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX> <MODEL_ITER>
```

3. Evaluate the model with spatial dynamic filters and response loss (and caption loss):
```
./experiments/scripts/eval_response.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX> <MODEL_ITER>
```

4. Evaluate the model with spatial dynamic filters and response loss for VGG16 and Faster R-CNN:
```
./experiments/scripts/eval_vgg.sh <GPUID> <DATASET> <SPLITBY> <OUTPUT_POSTFIX> <MODEL_ITER>
```

## Acknowledgement
Thanks for the work of [Licheng Yu](https://www.cs.unc.edu/~licheng/). Our code is heavily borrowed from the implementation of [MattNet](https://github.com/lichengunc/MAttNet).

## Note
The model and code are available for non-commercial research purposes only.
* 9/2019: code released

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