---
language:
- en
task_categories:
- video-text-to-text
- visual-question-answering
tags:
- video
- question-answering
- visual-question-answering
- audio-visual
- situated-reasoning
- benchmark
- multimodal
- datasets
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
multilinguality:
- monolingual
source_datasets:
- original
pretty_name: 'QIVD: Qualcomm Interactive Video Dataset'
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: metadata.parquet
dataset_info:
  features:
  - name: video_file_name
    dtype: string
  - name: id
    dtype: int64
  - name: category
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: short_answer
    dtype: string
  - name: timestamp
    dtype: string
  splits:
  - name: train
    num_examples: 2900
license: other
---

# QIVD: Qualcomm Interactive Video Dataset

[![arXiv](https://img.shields.io/badge/arXiv-2503.19356-b31b1b.svg)](https://arxiv.org/abs/2503.19356)
[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://www.qualcomm.com/developer/software/qualcomm-interactive-video-dataset-qivd)

![](https://q-ivd.github.io/assets/img/qivd/teaser_noname.png)

A collection of 2,900 video clips paired with visual question-answer annotations.
Each clip is associated with exactly one question drawn from one of 13 fine-grained QA categories,
a full-sentence answer, a concise short answer, and a timestamp pinpointing the relevant moment in the video.

## Overview

QIVD is a dataset and benchmark for online, situated audio-visual question answering. Unlike existing video QA benchmarks that operate in an offline paradigm (full video + question given at once), QIVD captures a genuinely interactive setup: crowd workers recorded short egocentric clips while simultaneously speaking a question into the camera. The AI system must answer in real time from the audio-visual stream, identifying both what to answer and when to start answering.

## Dataset Structure

```
├── metadata.parquet
└── videos/
    ├── 00000000.mp4
    ├── 00000001.mp4
    └── ...
```


### Schema

| Column | Type | Description |
|---|---|---|
| `video_file_name` | string | Repo-relative path to the video (`videos/XXXXXXXX.mp4`) |
| `id` | int64 | Unique annotation identifier |
| `category` | string | One of 13 semantic QA categories (see below) |
| `question` | string | Transcribed question spoken during recording |
| `answer` | string | Full natural-language answer |
| `short_answer` | string | Concise answer for exact-match evaluation; `"NA"` when no short form applies |
| `timestamp` | string | `MM:SS.s` — earliest moment in the clip when the question can be correctly answered |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("Qualcomm-AI-Research/QIVD")
print(ds["train"][0])
# {
#   'video_file_name': 'videos/00000000.mp4',
#   'id': 1972,
#   'category': 'object referencing',
#   'question': "What am I holding in my left hand?",
#   'answer': "You are holding a Rubik's cube in your left hand.",
#   'short_answer': "A Rubik's cube",
#   'timestamp': '00:04.4'
# }
```

To download a specific video:

```python
from huggingface_hub import hf_hub_download

video_path = hf_hub_download(
    repo_id="Qualcomm-AI-Research/QIVD",
    filename="videos/00000000.mp4",
    repo_type="dataset",
)
```

## Dataset License

This dataset is released for research purposes only. Use of the dataset is subject to the license terms of the Qualcomm Interactive Video Dataset. Please refer to the [accompanying license documentation](https://huggingface.co/datasets/Qualcomm-AI-Research/QIVD/blob/main/license.pdf) for full terms, conditions, and usage restrictions.


## Citation

```bibtex
@inproceedings{pourreza2026can,
    title={Can Vision-Language Models Answer Face to Face Questions in the Real-World?},
    author={Reza Pourreza and Rishit Dagli and Apratim Bhattacharyya and Sunny Panchal and Guillaume Berger and Roland Memisevic},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026},
    url={https://openreview.net/forum?id=I3dPEvbp8o}
}
```