README.md

metadata

license: cc-by-4.0
task_categories:
  - image-text-to-text
  - image-to-text
  - text-classification
language:
  - en
tags:
  - Image
  - Captioning
  - Violence
  - Detection
  - Vision
  - AI
  - Data
  - Sceince
  - Deep
  - Learning
pretty_name: 'Violence-Cap: The First Frame-Level Captioning Dataset for Violence Research'
size_categories:
  - 10K<n<100K

Violence-Cap Dataset

Violence-Cap is a video frame captioning dataset layered over the Real Life Violence Situations (RLVS) dataset. It is designed to provide granular, frame-level context for video violence analysis.

Each record in the dataset pairs an individual video frame with:

1. A descriptive text caption.
2. An independent, frame-level binary violence label.

Motivation & Key Findings

Video-level labels often fail to capture the temporal nuances of an event. In the creation of this dataset, 5,688 frames were extracted from videos globally labeled as violent. Upon human review, 1,243 frames (21.9%) were identified as non-violent. This highlights the critical necessity of per-frame annotation when training precise violence recognition and captioning models.

Dataset Statistics & Splits

The dataset features pre-defined, video-level train, validation, and test splits to ensure no data leakage between frames of the same video during model evaluation.

• Curated Set: 10,907 frame–caption–label triples across 1,991 videos.
• Full Set: 10,952 frame–caption–label triples across all 2,000 videos.

Files and Annotations

The annotations are distributed across two primary CSV files:

File Name	Rows (Triples)	Video Count	Description
curated_set.csv	10,907	1,991	The thoroughly reviewed and cleaned dataset recommended for training and evaluation.
full_set.csv	10,952	2,000	The complete, unpruned dataset spanning all original RLVS videos.

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license.

Related Work

Violence-Cap is built upon the RLVS dataset.

Soliman, M. M., Hussein Kamal, M., Abd El-Massih Nashed, M., Mostafa, Y. M., Safwat Chawky, B., & Khattab, D. (2019). Violence recognition from videos using deep learning techniques. 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), 80–85.