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Vision-Language Transformer and Query Generation for Referring Segmentation

Please consider citing our paper in your publications if the project helps your research.

@inproceedings{vision-language-transformer,
  title={Vision-Language Transformer and Query Generation for Referring Segmentation},
  author={Ding, Henghui and Liu, Chang and Wang, Suchen and Jiang, Xudong},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2021}
}

Introduction

Vision-Language Transformer (VLT) is a framework for referring segmentation task. Our method produces multiple query vector for one input language expression, and use each of them to “query” the input image, generating a set of responses. Then the network selectively aggregates these responses, in which queries that provide better comprehensions are spotlighted.

Installation

1. Environment:

   • Python 3.6

   • tensorflow 1.15

   • Other dependencies in requirements.txt

   • SpaCy model for embedding:

     python -m spacy download en_vectors_web_lg

2. Dataset preparation

   • Put the folder of COCO training set ("train2014") under data/images/.

   • Download the RefCOCO dataset from here and extract them to data/. Then run the script for data preparation under data/:

     cd data
     python data_process_v2.py --data_root . --output_dir data_v2 --dataset [refcoco/refcoco+/refcocog] --split [unc/umd/google] --generate_mask
     

Evaluating

1. Download pretrained models & config files from here.

2. In the config file, set:

   • evaluate_model: path to the pretrained weights
   • evaluate_set: path to the dataset for evaluation.

3. Run

   python vlt.py test [PATH_TO_CONFIG_FILE]
   

Training

1. Pretrained Backbones: We use the backbone weights proviede by MCN.

   Note: we use the backbone that excludes all images that appears in the val/test splits of RefCOCO, RefCOCO+ and RefCOCOg.

2. Specify hyperparameters, dataset path and pretrained weight path in the configuration file. Please refer to the examples under /config, or config file of our pretrained models.

3. Run

   python vlt.py train [PATH_TO_CONFIG_FILE]
   

Acknowledgement

We borrowed a lot of codes from MCN, keras-transformer, RefCOCO API and keras-yolo3. Thanks for their excellent works!