Zero Shot Detection Dataset

This repository contains the datasets used in Zero Shot Detection by Pengkai Zhu, Hanxiao Wang, Tolga Bolukbasi and Venkatesh Saligrama.

@article{Zhu_2019,
   title={Zero Shot Detection},
   DOI={10.1109/tcsvt.2019.2899569},
   journal={IEEE Transactions on Circuits and Systems for Video Technology},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Zhu, Pengkai and Wang, Hanxiao and Saligrama, Venkatesh},
   year={2019}
}

Seen/Unseen Split

The scripts can download Pascal VOC or MSCOCO and split it into four parts as in the paper:

• Train: seen in train
• Test-Seen: seen in val/test
• Test-Unseen: unseen in train&val&test
• Test-Mix: both seen & unseen in val/test

The dataset is split based on assigned seen categories names. We provide the splits we used in the paper in seen_names subfolder.

Attributes

The attributes for Pascal VOC will be downloaded and extracted automatically when running get_voc_zsd_dataset.sh. We also provide the attributes we use in the paper in the attributes subfolder:

• coco_w2v.txt: w2v attributes for coco categories
• coco_w2v_voc.txt: projected w2v attributes for coco categories (mirroring VOC attributes similarity)
• voc.txt: labelled attributes (from aP&Y) for VOC categories
• voc_w2v.txt: w2v attributes for VOC categories

How to use

• Preliminary: numpy

Setup Pascal VOC:

bash get_voc_zsd_dataset.sh $zsd-data-dir  # $zsd-data-dir: directory for saving pascal ZSD dataset

The dataset will be downloaded to $zsd-data-dir and the split sets will be saved in 1010split subfolder by default. If you already downloaded the dataset or would like to try some other splits, just run:

python zsd_split.py --dataset voc --data_dir $zsd-data-dir --name_file voc.names \
--seen_name_file seen_names/voc/${choose another split} \
--save_dir ${split save name} \

Setup MSCOCO

bash get_coco_zsd_dataset.sh $zsd-data-dir  # $zsd-data-dir: directory for saving coco ZSD dataset