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Phrase Localization Evaluation Toolkit

Introduction

This repository contains the libraries and scripts used for evaluating phrase localization in my ICCV 2019 paper (Phrase Localization Without Paired Training Examples).

I found existing evaluation scripts for the phrase localization task difficult to setup and use. I only really needed to evaluate my output, not to run other people's models so that I can evaluate my output! Thus, this toolkit was born!

I wrote it in standard Python, so the script does not need any other dependencies. You only need to provide the script a list of ground truth bounding boxes and a list of predicted bounding boxes, and it will return the accuracy. I hope having a simple and standard evaluation script for the task will make life easier for everyone!

Using the toolkit

The toolkit is a Python module located in the lib/ directory. Please refer to the doc comments in the code for explanations and usage.

Ground truth annotations for various datasets are provided in the data/ directory.

An example script is available as demo.py.

Citation

If you use this evaluation toolkit, please cite the following work:

Josiah Wang and Lucia Specia (2019). Phrase Localization Without Paired Training Examples. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

@InProceedings{WangSpecia:2019,
author    = {Wang, Josiah and Specia, Lucia},
title     = {Phrase Localization Without Paired Training Examples},
booktitle = {Proceedings of the IEEE/CVF Internaitonal Conference on Computer Vision (ICCV)},
publisher = {{IEEE}},
month     = oct,
year      = {2019},
pages     = {},  
address   = {Seoul, South Korea}
}

License

GNU General Public License v3.0