Awesome

The Novel Visual Concept (NVC) dataset

project page

Introduction

This package provides annotations and a simple toolkit for the Novel Visual Concept (NVC) dataset.

Please run setup.sh to download images and pre-calculated VggNet layer 15 image features.

For more details, please run NVC_dataset_demo.ipynb. You are also welcome to look into the python class in ./python_lib/class_nvc_dataset.py

If you get a "bad request" error message when opening NVC_dataset_demo.ipynb, you probably need to update your ipython. You can also run NVC_dataset_demo.py instead.

Citation

If you find this dataset and annotations useful in your research, please consider citing:

@inproceedings{mao2015learning,
  title={Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images},
  author={Mao, Junhua and Xu, Wei and Yang, Yi and Wang, Jiang and Huang, Zhiheng and Yuille, Alan},
  booktitle={ICCV},
  year={2015}
}

Format for the annotations

This annotations contains two JSON files (in ./annotations): one for training and validation set, one for testing set. The JSON file is organized as follows:

The root is a key-value dictionary:

• 'version': version of the dataset
• 'concepts': a list of the novel visual concepts
• 'images': a list, each element of the list is a dictionary:
  • 'concept': novel concepts for the image
  • 'image_id': unique id for the image
  • 'image_name': file name for the image
  • 'train_val_test_split': 'train' or 'val' or 'test'
  • 'sentences': a list, each element of the list is a dictionary:
    • 'raw': a string which is the raw annotated sentence
    • 'tokens': tokenized sentence, NOT includes the period at the end
    • 'sentence_id': unique id for the sentence
    • 'image_id': unique image id that the sentence belongs

License

The annotations in this dataset belong to University of California, Los Angeles and Baidu Research, and are licensed under a Creative Commons Attribution 4.0 License.