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--- |
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dataset_info: |
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features: |
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- name: obs_uid |
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dtype: string |
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- name: usr_uid |
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dtype: string |
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- name: caption |
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dtype: string |
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- name: image |
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dtype: image |
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- name: clicks_path |
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sequence: |
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sequence: int32 |
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length: 2 |
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- name: clicks_time |
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sequence: timestamp[s] |
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splits: |
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- name: train |
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num_bytes: 1611467 |
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num_examples: 3848 |
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download_size: 241443505 |
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dataset_size: 1611467 |
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--- |
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### Dataset Description |
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CapMIT1003 is a dataset of captions and click-contingent image explorations collected during captioning tasks. |
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CapMIT1003 is based on the same stimuli from the well-known MIT1003 benchmark, for which eye-tracking data |
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under free-viewing conditions is available, which offers a promising opportunity to concurrently study human attention under both tasks. |
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### Usage |
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You can load CapMIT1003 as follows: |
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```python |
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from datasets import load_dataset |
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capmit1003_dataset = load_dataset("azugarini/CapMIT1003", trust_remote_code=True) |
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print(capmit1003_dataset["train"][0]) #print first example |
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``` |
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### Citation Information |
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If you use this dataset in your research or work, please cite the following paper: |
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``` |
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@article{zanca2023contrastive, |
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title={Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors}, |
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author={Zanca, Dario and Zugarini, Andrea and Dietz, Simon and Altstidl, Thomas R and Ndjeuha, Mark A Turban and Schwinn, Leo and Eskofier, Bjoern}, |
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journal={arXiv preprint arXiv:2305.12380}, |
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year={2023} |
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``` |
