| | --- |
| | annotations_creators: |
| | - crowdsourced |
| | language: |
| | - en |
| | multilinguality: |
| | - monolingual |
| | pretty_name: KiloGram |
| | size_categories: |
| | - 1K<n<10K |
| | source_datasets: |
| | - original |
| | tags: |
| | - tangrams |
| | - reference-games |
| | - vision-language |
| | viewer: false |
| | --- |
| | |
| | Preprocessed training and evaluation data from KiloGram. |
| |
|
| | KiloGram dataset and code repo: https://github.com/lil-lab/kilogram |
| |
|
| | --- |
| | # File Formats |
| | ## Training Set |
| | Texts: `train_*.json` are all in the format of `{tangramName: list(annotations)}`. |
| |
|
| | Images: Colored images with parts (under `/color`) are named in the format of `tangramName_{idx}.png`, where `idx` corresponds to the index of the annotation in the text file. |
| |
|
| | ## Validation, Development, Heldout Set |
| |
|
| | Texts: `{whole, part}_{black, color}.json` are in the format of `{"targets": list(imageFileNames), "images": list(imageFileNames), "texts": list(annotations)}`. We flattened all the contexts and concatenated them into one list for each entry. |
| |
|
| | E.g. the first 10 elements in `"targets"` are the image file name of the target of the first context repeated 10 times; the first 10 of `"images"` are the image file names in that context; and the first 10 of `"texts"` are the corresponding 10 annotations in that context. |
| |
|
| | `/controlled` contains experiments with constrained contexts controlled for number of parts, and `/random` contains ones without. (See Appendix A.8 in paper) |
| |
|
| | `/development/texts/augmented/aug_dev.json` and `images/augmented.tar.bz2` are experiments in the same format as above used to evaluate the effect of adding part information. |
| |
|
| |
|
| | Intermediate files: |
| |
|
| | `*/text/controlled/eval_batch_data.json` are in the format of |
| | `{tangramName: {numOfParts: list({"target": [tangramName_{idx}, annotation], "distractors": list(list([tangramName_{idx}, annotation]))})}}`, used to generate controlled experiment jsons. Note: annotations are descriptions concatenated by "#" instead of in natural English. |
| |
|
| | # Citation |
| |
|
| | ```bibtex |
| | @misc{ji2022abstractvisualreasoningtangram, |
| | title={Abstract Visual Reasoning with Tangram Shapes}, |
| | author={Anya Ji and Noriyuki Kojima and Noah Rush and Alane Suhr and Wai Keen Vong and Robert D. Hawkins and Yoav Artzi}, |
| | year={2022}, |
| | eprint={2211.16492}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CL}, |
| | url={https://arxiv.org/abs/2211.16492}, |
| | } |
| | ``` |
