Awesome

NewsCLIPpings Dataset

Our dataset with automatically generated out-of-context image-caption pairs in the news media. For inquiries and requests, please contact graceluo@berkeley.edu.

Requirements

Make sure you are running Python 3.6+.

Getting Started

1. Request the VisualNews Dataset. Place the files under the visual_news folder.
2. Run ./download.sh to download our matches and populate the news_clippings folder (place into news_clippings/data/).
3. Consider doing analyses of your own using the embeddings we have provided (place into news_clippings/embeddings/).

All of the ids and image paths provided in our data/ folder exactly correspond to those listed in the data.json file in VisualNews.

Your file structure should look like this:

news_clippings
│
└── data/
└── embeddings/

visual_news
│
└── origin/
│    └── data.json
│        ...
└── ...

Data Format

The data is ordered such that every even sample is pristine, and the next sample is its associated falsified sample.

• id: the id of the VisualNews sample associated with the caption
• image_id: the id of the VisualNews sample associated with the image
• similarity_score: the similarity measure used to generate the sample (i.e. clip_text_image, clip_text_text, sbert_text_text, resnet_place)
• falsified: a binary indicator if the caption / image pair was the original pair in VisualNews or a mismatch we generated
• source_dataset (Merged / Balanced only): the index of the sub-split name in source_datasets

Here's an example of how you can start using our matches:

    import json
    visual_news_data = json.load(open("visualnews/origin/data.json"))
    visual_news_data_mapping = {ann["id"]: ann for ann in visual_news_data}
    
    data = json.load(open("news_clippings/data/merged_balanced/val.json"))
    annotations = data["annotations"]
    ann = annotations[0]
    
    caption = visual_news_data_mapping[ann["id"]]["caption"]
    image_path = visual_news_data_mapping[ann["image_id"]]["image_path"]
    
    print("Caption: ", caption)
    print("Image Path: ", image_path)
    print("Is Falsified: ", ann["falsified"])

Embeddings

We include the following precomputed embeddings:

• clip_image_embeddings: 512-dim image embeddings from CLIP ViT-B/32. <br /> Contains embeddings for samples in all splits.
• clip_text_embeddings: 512-dim caption embeddings from CLIP ViT-B/32. <br /> Contains embeddings for samples in all splits.
• sbert_embeddings: 768-dim caption embeddings from SBERT-WK. <br /> Contains embeddings for samples in all splits.
• places_resnet50: 2048-dim image embeddings using ResNet50 trained on Places365. <br /> Contains embeddings only for samples in the scene_resnet_place split (where [PERSON] entities were not detected in the caption).

The following embedding types were not used in the construction of our dataset, but you may find them useful.

• facenet_embeddings: 512-dim embeddings for each face detected in the images using FaceNet. If no faces were detected, returns None. <br /> Contains embeddings only for samples in the person_sbert_text_text split (where [PERSON] entities were detected in the caption).

All embeddings are dictionaries of {id: numpy array} stored in pickle files for train / val / test. You can access the features for each image / caption by its id like so:

    import pickle
    clip_image_embeddings = pickle.load(open("news_clippings/embeddings/clip_image_embeddings/test.pkl", "rb"))
    id = 701864
    print(clip_image_embeddings[id])

Metadata

We have additional metadata, such as the spaCy and REL named entities, timestamp, location of the original article content, etc.

Training

To run the benchmarking experiments we reported in our paper, look at the README for news_clippings_training/.

Citing

If you find our dataset useful for your research, please, cite the following paper:

@article{luo2021newsclippings,
  title={NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media},
  author={Luo, Grace and Darrell, Trevor and Rohrbach, Anna},
  journal={arXiv:2104.05893},
  year={2021}
}