Code release for Hu et al. Segmentation from Natural Language Expressions. in ECCV, 2016
Jupyter Notebook Python Shell
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.

README.md

Segmentation from Natural Language Expressions

This repository contains the code for the following paper:

  • R. Hu, M. Rohrbach, T. Darrell, Segmentation from Natural Language Expressions. in ECCV, 2016. (PDF)
@article{hu2016segmentation,
  title={Segmentation from Natural Language Expressions},
  author={Hu, Ronghang and Rohrbach, Marcus and Darrell, Trevor},
  journal={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2016}
}

Project Page: http://ronghanghu.com/text_objseg

Installation

  1. Install Google TensorFlow (v1.0.0 or higher) following the instructions here.
  2. Download this repository or clone with Git, and then cd into the root directory of the repository.

Demo

  1. Download the trained models:
    exp-referit/tfmodel/download_trained_models.sh.
  2. Run the language-based segmentation model demo in ./demo/text_objseg_demo.ipynb with Jupyter Notebook (IPython Notebook).

Image

Training and evaluation on ReferIt Dataset

Download dataset and VGG network

  1. Download ReferIt dataset:
    exp-referit/referit-dataset/download_referit_dataset.sh.
  2. Download VGG-16 network parameters trained on ImageNET 1000 classes:
    models/convert_caffemodel/params/download_vgg_params.sh.

Training

  1. You may need to add the repository root directory to Python's module path: export PYTHONPATH=.:$PYTHONPATH.
  2. Build training batches for bounding boxes:
    python exp-referit/build_training_batches_det.py.
  3. Build training batches for segmentation:
    python exp-referit/build_training_batches_seg.py.
  4. Select the GPU you want to use during training:
    export GPU_ID=<gpu id>. Use 0 for <gpu id> if you only have one GPU on your machine.
  5. Train the language-based bounding box localization model:
    python exp-referit/exp_train_referit_det.py $GPU_ID.
  6. Train the low resolution language-based segmentation model (from the previous bounding box localization model):
    python exp-referit/init_referit_seg_lowres_from_det.py && python exp-referit/exp_train_referit_seg_lowres.py $GPU_ID.
  7. Train the high resolution language-based segmentation model (from the previous low resolution segmentation model):
    python exp-referit/init_referit_seg_highres_from_lowres.py && python exp-referit/exp_train_referit_seg_highres.py $GPU_ID.

Alternatively, you may skip the training procedure and download the trained models directly:
exp-referit/tfmodel/download_trained_models.sh.

Evaluation

  1. Select the GPU you want to use during testing: export GPU_ID=<gpu id>. Use 0 for <gpu id> if you only have one GPU on your machine. Also, you may need to add the repository root directory to Python's module path: export PYTHONPATH=.:$PYTHONPATH.
  2. Run evaluation for the high resolution language-based segmentation model:
    python exp-referit/exp_test_referit_seg.py $GPU_ID
    This should reproduce the results in the paper.
  3. You may also evaluate the language-based bounding box localization model:
    python exp-referit/exp_test_referit_det.py $GPU_ID
    The results can be compared to this paper.