---
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}
```
![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F6621462611c923d051d62072%2FqC8oKkhoFqyYNY5ACTSqX.png)


## 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}, 
}
```