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--- |
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license: apache-2.0 |
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size_categories: |
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- 1K<n<10K |
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dataset_info: |
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features: |
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- name: id |
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dtype: int32 |
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- name: image |
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dtype: image |
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- name: sensor_type |
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dtype: string |
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- name: question_type |
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dtype: string |
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- name: question |
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dtype: string |
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- name: question_query |
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dtype: string |
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- name: answer |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1455392605.0 |
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num_examples: 6248 |
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download_size: 903353168 |
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dataset_size: 1455392605.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# SPARK (multi-vision Sensor Perception And Reasoning benchmarK) |
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<!-- Provide a quick summary of the dataset. --> |
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SPARK can reduce the fundamental multi-vision sensor information gap between images and multi-vision sensors. We generated 6,248 vision-language test samples automatically to investigate multi-vision sensory perception and multi-vision sensory reasoning on physical sensor knowledge proficiency across different formats, covering different types of sensor-related questions. |
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## Dataset Details |
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## Uses |
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<!-- Address questions around how the dataset is intended to be used. --> |
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### Direct Use |
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<!-- This section describes suitable use cases for the dataset. --> |
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### Source Data |
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#### Data Collection and Processing |
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These instructions are built from five public datasets: [MS-COCO](https://arxiv.org/abs/1405.0312), [M3FD](https://arxiv.org/abs/2203.16220v1), [Dog&People](https://public.roboflow.com/object-detection/thermal-dogs-and-people), [RGB-D scene dataset](https://arxiv.org/abs/2110.11590), and [UNIFESP X-ray Body Part Classifier Competition dataset](https://www.kaggle.com/competitions/unifesp-x-ray-body-part-classifier). |
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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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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Contact |
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[SangYun Chung](https://sites.google.com/view/sang-yun-chung/profile): [email protected] |
