README.md

metadata

license: cc-by-nc-sa-4.0
task_categories:
  - object-detection
language:
  - en
tags:
  - hallucination-detection
  - Hallucination Mitigation
  - MLLMs
  - MSLMs
  - Multimodal and crossmodal learning
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: captions
      dtype: string
  splits:
    - name: train
      num_bytes: 2056662370.576
      num_examples: 1256
  download_size: 2130210948
  dataset_size: 2056662370.576
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Aerial Mirage: Unmasking Hallucinations in Large Vision Language Models

This repository hosts the AeroCaps dataset as a Hugging Face dataset.

The lack of image-caption datasets for drone imagery poses a significant challenge for training and evaluating drone image captioning. To address this gap, we contribute the first Aerial-view Image Captioning dataset. This contains atleast four captions per image. AeroCaps is introduced in WACV 2025.

Dataset Structure

Column	Type	Description
image	Image	Aerial-view photograph
captions	string	Comma-separated reference captions

Usage

from datasets import load_dataset

ds = load_dataset("NLIP-lab/AeroCaps")
sample = ds["train"][0]
print(sample["captions"])
sample["image"].show()

📜 Citation

If you use AeroCaps in your research, please cite:

@InProceedings{Debolena_WACV25,
    author    = {Basak, Debolena and Bhatt, Soham and Kanduri, Sahith and Desarkar, Maunendra Sankar},
    title     = {Aerial Mirage: Unmasking Hallucinations in Large Vision Language Models},
    booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)},
    month     = {February},
    year      = {2025},
    pages     = {5500-5508}
}

---

⚖️ License

The AeroCaps dataset is intended for research purposes. Please see the the HF dataset card for terms.