We are doing some further cleaning on refcoco+, thus this dataset may change in the future. We will also release refcelf split by UC Berkeley in the future.
If you used the following three datasets RefClef, RefCOCO and RefCOCO+, please consider cite our EMNLP2014 paper; if you want to compare with our recent results, please check our ECCV2016 paper.
Kazemzadeh, Sahar, et al. "ReferItGame: Referring to Objects in Photographs of Natural Scenes." EMNLP 2014.
Yu, Licheng, et al. "Modeling Context in Referring Expressions." ECCV 2016.Run "make" before using the code.
It will generate _mask.c and _mask.so in external/ folder.
These mask-related codes are copied from mscoco API.
Download the cleaned data and extract them into "data" folder
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Besides we add "mscoco" into the "data/images" folder. Download it from mscoco This dataset is for refcoco, refcoco+ and refgoogle. For refclef, we add "saiapr_tc-12" into 'data/images' folder. I only extracted the related images as a subset of the original imageCLEF, i.e., 19997 images. Please download the subset from here (http://tlberg.cs.unc.edu/licheng/referit/data/images/saiapr_tc-12.zip). The "refer.py" is able to load all 4 datasets with different kinds of data split by UNC, Google and UC Berkeley.
# locate your own data_root, and choose the dataset_splitBy you want to use
refer = REFER(data_root, dataset='refclef', splitBy='unc')
refer = REFER(data_root, dataset='refcoco', splitBy='unc')
refer = REFER(data_root, dataset='refcoco', splitBy='google')
refer = REFER(data_root, dataset='refcoco+', splitBy='unc')
refer = REFER(data_root, dataset='refcocog', splitBy='google')