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