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