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---
task_categories:
- question-answering
- zero-shot-classification
pretty_name: I Don't Know Visual Question Answering
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answers
struct:
- name: I don't know
dtype: int64
- name: 'No'
dtype: int64
- name: 'Yes'
dtype: int64
splits:
- name: val
num_bytes: 395276320
num_examples: 502
download_size: 40823223
dataset_size: 395276320
configs:
- config_name: default
data_files:
- split: val
path: data/val-*
license: apache-2.0
language:
- en
tags:
- VQA
- Multimodal
---
# I Don't Know Visual Question Answering - IDKVQA dataset - ICCV 25
<!-- Provide a quick summary of the dataset. -->
We introduce IDKVQA, an embodied dataset specifically designed and annotated for visual question answering using the agent’s observations during navigation,
where the answer includes not only ```Yes``` and ```No```, but also ```I don’t know```.
## Dataset Details
Please see our ICCV 25 accepted paper: [```Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues```](https://arxiv.org/abs/2412.01250)
For more information, visit our [Github repo.](https://github.com/intelligolabs/CoIN)
**Curated by:** [Francesco Taioli](https://francescotaioli.github.io/) and [Edoardo Zorzi](https://huggingface.co/e-zorzi).
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
The dataset contains 502 rows and only one split ('val').
Each row is a triple (image, question, answers), where 'image' is the image which 'question' refers to, and 'answers' is a dictionary mapping each possible answer (```Yes```, ```No```, ```I don't know```) to the number of annotators picking that answer.
```
DatasetDict({
val: Dataset({
features: ['image', 'question', 'answers'],
num_rows: 502
})
})
```
## Visualization
```
from datasets import load_dataset
idkvqa = load_dataset("ftaioli/IDKVQA")
sample_index = 42
split = "val"
row = idkvqa[split][sample_index]
image = row["image"]
question = row["question"]
answers = row["answers"]
print(question), print(answers)
image
```
You will obtain:
```
Does the couch have a tufted backrest? You must answer only with Yes, No, or ?=I don't know.
{"I don't know": 0, 'No': 0, 'Yes': 3}
```
## Uses
You can use this dataset to train or test a model's visual-question answering capabilities about everyday objects.
To reproduce the baselines in our paper [```Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues```](https://arxiv.org/abs/2412.01250), please check the README in the [official repository](https://github.com/intelligolabs/CoIN).
<!-- Address questions around how the dataset is intended to be used. -->
<!-- ## Dataset Structure -->
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
<!-- [More Information Needed] -->
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@misc{taioli2025collaborativeinstanceobjectnavigation,
title={Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues},
author={Francesco Taioli and Edoardo Zorzi and Gianni Franchi and Alberto Castellini and Alessandro Farinelli and Marco Cristani and Yiming Wang},
year={2025},
eprint={2412.01250},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2412.01250},
}
``` |