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--- |
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annotations_creators: |
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- crowdsourced |
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language: |
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- en |
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multilinguality: |
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- monolingual |
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pretty_name: KiloGram |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- original |
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tags: |
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- tangrams |
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- reference-games |
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- vision-language |
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viewer: false |
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--- |
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Preprocessed training and evaluation data from KiloGram. |
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KiloGram dataset and code repo: https://github.com/lil-lab/kilogram |
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--- |
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# File Formats |
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## Training Set |
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Texts: `train_*.json` are all in the format of `{tangramName: list(annotations)}`. |
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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. |
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## Validation, Development, Heldout Set |
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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. |
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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. |
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`/controlled` contains experiments with constrained contexts controlled for number of parts, and `/random` contains ones without. (See Appendix A.8 in paper) |
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`/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. |
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Intermediate files: |
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`*/text/controlled/eval_batch_data.json` are in the format of |
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`{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. |
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# Citation |
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|
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```bibtex |
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@misc{ji2022abstractvisualreasoningtangram, |
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title={Abstract Visual Reasoning with Tangram Shapes}, |
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author={Anya Ji and Noriyuki Kojima and Noah Rush and Alane Suhr and Wai Keen Vong and Robert D. Hawkins and Yoav Artzi}, |
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year={2022}, |
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eprint={2211.16492}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2211.16492}, |
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} |
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``` |
