# PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

[Project](https://polyformer.github.io/) | [GitHub](https://github.com/amazon-science/polygon-transformer) | [Demo](https://huggingface.co/spaces/koajoel/PolyFormer)

## Model description

PolyFormer is a unified framework for referring image segmentation (RIS) and referring expression comprehension (REC) by formulating them as a sequence-to-sequence (seq2seq) prediction problem. For more details, please refer to our paper: 

[PolyFormer: Referring Image Segmentation as Sequential Polygon Generation](https://arxiv.org/abs/2302.07387) 
Jiang Liu*, Hui Ding*, Zhaowei Cai, Yuting Zhang, Ravi Kumar Satzoda, Vijay Mahadevan, R. Manmatha, [CVPR 2023](https://cvpr2023.thecvf.com/Conferences/2023/AcceptedPapers)

## Training data

We pre-train PolyFormer on the REC task using Visual Genome, RefCOCO, RefCOCO+, RefCOCOg, and Flickr30k-entities, and the finetune on REC + RIS task using RefCOCO, RefCOCO+,
and RefCOCOg.

* PolyFormer-B: Swin-B as the visual encoder, BERT-base as the text encoder, 6 transformer encoder layers and 6 decoder layers.
* PolyFormer-L: Swin-L as the visual encoder, BERT-base as the text encoder, 12 transformer encoder layers and 12 decoder layers.

## Citation

If you find PolyFormer useful in your research, please cite the following paper:

``` latex
@article{liu2023polyformer,
  title={PolyFormer: Referring Image Segmentation as Sequential Polygon Generation},
  author={Liu, Jiang and Ding, Hui and Cai, Zhaowei and Zhang, Yuting and Satzoda, Ravi Kumar and Mahadevan, Vijay and Manmatha, R},
  journal={arXiv preprint arXiv:2302.07387},
  year={2023}
}
```
{"license": "apache-2.0"}
arxiv:2302.07387 license:apache-2.0 has_space region:us
TAGS
#arxiv-2302.07387 #license-apache-2.0 #has_space #region-us 

# PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

Project | GitHub | Demo

## Model description

PolyFormer is a unified framework for referring image segmentation (RIS) and referring expression comprehension (REC) by formulating them as a sequence-to-sequence (seq2seq) prediction problem. For more details, please refer to our paper: 

PolyFormer: Referring Image Segmentation as Sequential Polygon Generation 
Jiang Liu*, Hui Ding*, Zhaowei Cai, Yuting Zhang, Ravi Kumar Satzoda, Vijay Mahadevan, R. Manmatha, CVPR 2023

## Training data

We pre-train PolyFormer on the REC task using Visual Genome, RefCOCO, RefCOCO+, RefCOCOg, and Flickr30k-entities, and the finetune on REC + RIS task using RefCOCO, RefCOCO+,
and RefCOCOg.

* PolyFormer-B: Swin-B as the visual encoder, BERT-base as the text encoder, 6 transformer encoder layers and 6 decoder layers.
* PolyFormer-L: Swin-L as the visual encoder, BERT-base as the text encoder, 12 transformer encoder layers and 12 decoder layers.

If you find PolyFormer useful in your research, please cite the following paper:


# PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

Project | GitHub | Demo ## Model description

PolyFormer is a unified framework for referring image segmentation (RIS) and referring expression comprehension (REC) by formulating them as a sequence-to-sequence (seq2seq) prediction problem. For more details, please refer to our paper: 

PolyFormer: Referring Image Segmentation as Sequential Polygon Generation 
Jiang Liu*, Hui Ding*, Zhaowei Cai, Yuting Zhang, Ravi Kumar Satzoda, Vijay Mahadevan, R. Manmatha, CVPR 2023 ## Training data

We pre-train PolyFormer on the REC task using Visual Genome, RefCOCO, RefCOCO+, RefCOCOg, and Flickr30k-entities, and the finetune on REC + RIS task using RefCOCO, RefCOCO+,
and RefCOCOg.

* PolyFormer-B: Swin-B as the visual encoder, BERT-base as the text encoder, 6 transformer encoder layers and 6 decoder layers.
* PolyFormer-L: Swin-L as the visual encoder, BERT-base as the text encoder, 12 transformer encoder layers and 12 decoder layers.

If you find PolyFormer useful in your research, please cite the following paper:
TAGS
#arxiv-2302.07387 #license-apache-2.0 #has_space #region-us 
 # PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

Project | GitHub | Demo ## Model description

PolyFormer is a unified framework for referring image segmentation (RIS) and referring expression comprehension (REC) by formulating them as a sequence-to-sequence (seq2seq) prediction problem. For more details, please refer to our paper: 

PolyFormer: Referring Image Segmentation as Sequential Polygon Generation 
Jiang Liu*, Hui Ding*, Zhaowei Cai, Yuting Zhang, Ravi Kumar Satzoda, Vijay Mahadevan, R. Manmatha, CVPR 2023 ## Training data

We pre-train PolyFormer on the REC task using Visual Genome, RefCOCO, RefCOCO+, RefCOCOg, and Flickr30k-entities, and the finetune on REC + RIS task using RefCOCO, RefCOCO+,
and RefCOCOg.

* PolyFormer-B: Swin-B as the visual encoder, BERT-base as the text encoder, 6 transformer encoder layers and 6 decoder layers.
* PolyFormer-L: Swin-L as the visual encoder, BERT-base as the text encoder, 12 transformer encoder layers and 12 decoder layers.

If you find PolyFormer useful in your research, please cite the following paper: