Dataset Card for PittImageVideoAdsDataset

Dataset Summary

PittImageVideoAdsDataset is the image and video advertisement dataset released with Automatic Understanding of Image and Video Advertisements. The paper reports 64,832 image advertisements and 3,477 YouTube advertisement videos, with human annotations for topics, sentiments, slogans, persuasive strategies, symbolic references, and action/reason Q/A. This Hugging Face version exposes the public annotation rows that can be reconstructed from the official released files.

This loader exposes two configurations:

• image_ads: annotated image advertisements. The image column is a datasets.Image feature loaded from the official Google Storage image ZIP files, and image_url preserves the corresponding Pitt source URL as text.
• video_ads: YouTube video IDs and URLs with raw annotator responses and cleaned majority-vote labels. Video files are not downloaded.

Supported Tasks and Leaderboards

The dataset supports advertisement understanding tasks such as topic recognition, sentiment recognition, persuasive strategy analysis, symbol detection, and action/reason response modeling. No active public leaderboard is bundled with this Hugging Face dataset.

Languages

The annotation text and Q/A responses are in English (en).

Dataset Structure

Data Instances

PittImageVideoAdsDataset provides image_ads and video_ads configurations. Load one configuration by passing its name:

import datasets as ds

dataset = ds.load_dataset(
    "creative-graphic-design/PittImageVideoAdsDataset",
    name="image_ads",
)

image_ads example:

{
  "id": "image-00000",
  "source_path": "0/10000.jpg",
  "image": "<remote image>",
  "image_url": "https://people.cs.pitt.edu/~mzhang/image_ads/0/10000.jpg",
  "qa_action": ["..."],
  "qa_reason": ["..."],
  "qa_combined_action_reason": [],
  "slogans": [],
  "topics": ["9", "9", "9"],
  "sentiments": [["12"], ["14"]],
  "strategies": [],
  "symbols": []
}

video_ads example:

{
  "id": "video-00000",
  "youtube_id": "KONL05sae4E",
  "youtube_url": "https://www.youtube.com/watch?v=KONL05sae4E",
  "raw_qa_action": ["Buy and wear this company's sports shoes and clothes."],
  "raw_qa_reason": ["..."],
  "raw_topics": ["media", "media"],
  "raw_sentiments": ["active"],
  "raw_funny": [0.0, 0.0, 0.0, 0.0, 0.0],
  "raw_exciting": [1.0, 1.0, 1.0, 1.0, 1.0],
  "raw_language": ["1", "1", "1", "1", "1"],
  "clean_topic": 27,
  "clean_sentiment": 12,
  "clean_funny": 0.0,
  "clean_exciting": 1.0,
  "clean_language": "1",
  "clean_effective": 1.0
}

Data Fields

image_ads fields:

• id (string): Stable row identifier generated by this loader.
• source_path (string): Original relative image path from the annotation files.
• image (Image): Advertisement image loaded from the official image ZIP files.
• image_url (string): Pitt source image URL derived from source_path.
• qa_action, qa_reason, qa_combined_action_reason, slogans, topics (list[string]): Free-form or class-id annotations.
• sentiments, strategies (list[list[string]]): Per-annotator class-id selections.
• symbols (list): Symbolic reference boxes with x1, y1, x2, y2, and label.

video_ads fields:

• id, youtube_id, youtube_url (string): Stable row identifier and YouTube reference.
• raw_qa_action, raw_qa_reason, raw_topics, raw_sentiments, raw_language (list[string]): Raw annotator responses or labels.
• raw_funny, raw_exciting (list[float]): Raw per-annotator binary selections.
• clean_topic, clean_sentiment (int32): Cleaned majority-vote class IDs.
• clean_funny, clean_exciting, clean_effective (float32): Cleaned binary labels.
• clean_language (string): Cleaned language label. Some videos do not have this field upstream and use an empty string.

Data Splits

Both configurations expose one train split because the upstream release is an annotation corpus and does not define train, validation, or test partitions. The train name is a Hugging Face packaging convention here; it is not an official training partition from the paper.

Config	Split	Rows
image_ads	train	64,454 annotated image paths
video_ads	train	3,477 videos

The row counts line up with the public files as follows:

Source count	Images	Videos
Paper-reported dataset size	64,832	3,477
Rows emitted by this loader	64,454	3,477

The image count is lower than the paper-reported total because this loader emits the union of image paths found in the public annotation JSON files. The remaining paper-reported images are not present as annotated rows in those JSON files, so the loader does not synthesize empty examples for them.

Dataset Creation

Curation Rationale

The dataset was created to study automatic understanding of advertisements beyond object recognition, including what action an ad encourages, why a viewer should take that action, topic, sentiment, and persuasive visual strategies.

Source Data

The original project collected image advertisements and YouTube advertisement videos. The official project page provides annotation ZIP files, video IDs, Pitt image URLs, and Google Storage image ZIP files. This loader downloads the official annotation ZIP files and image ZIP files, then derives Pitt image URLs directly from the annotation source_path values.

Annotations

The annotations were collected from human annotators, including free-form Q/A and slogans, multiple-choice topics and sentiments, symbolic reference boxes, and cleaned majority-vote labels for video-level attributes.

Personal and Sensitive Information

Advertisements may contain people, brands, public figures, health claims, political content, or other persuasive and sensitive themes. Users should inspect examples and labels before applying the dataset to downstream systems.

Considerations for Using the Data

Social Impact of Dataset

This dataset can help evaluate and improve models that reason about persuasive visual media. Such models may also be used for ad targeting, content moderation, or influence analysis, so downstream use should consider privacy, bias, and manipulation risks.

Discussion of Biases

The dataset reflects the distribution of advertisements collected by the original authors and the judgments of crowd annotators. Topics, sentiments, and persuasive strategies may encode cultural assumptions from the source ads and annotator pool.

Other Known Limitations

The image column embeds decoded image data when this dataset is published to the Hub. The image_url field remains a source reference and may be less stable from automated environments. The video configuration contains YouTube IDs and URLs, not video files, so availability may change if YouTube videos are removed or restricted.

Additional Information

Dataset Curators

The dataset was created by the authors of Automatic Understanding of Image and Video Advertisements. This Hugging Face dataset loader was added in the creative-graphic-design/huggingface-datasets repository.

Licensing Information

A dataset-specific license was not found on the official project page at the time this loader was created. This dataset card marks the dataset content license as unknown.

Citation Information

@inproceedings{hussain2017automatic,
  title={Automatic Understanding of Image and Video Advertisements},
  author={Hussain, Zaeem and Zhang, Mingda and Zhang, Xiaozhong and Ye, Keren and Thomas, Christopher and Agha, Zuha and Ong, Nathan and Kovashka, Adriana},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1705--1715},
  year={2017}
}

Contributions

Thanks to Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang, Keren Ye, Christopher Thomas, Zuha Agha, Nathan Ong, Adriana Kovashka, and the University of Pittsburgh project team for creating and releasing this dataset.