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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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language_creators: |
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- found |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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paperswithcode_id: vasr |
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pretty_name: VASR |
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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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- commonsense-reasoning |
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- visual-reasoning |
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task_ids: [] |
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extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes. The full license agreement is available in the dataset files." |
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--- |
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# Dataset Card for VASR |
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- [Dataset Description](#dataset-description) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [How to Submit Predictions?](#how-to-submit-predictions?) |
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- [Colab notebook code for VASR evaluation with ViT](#colab-notebook-code-for-vasr-evaluation-with-clip) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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VASR is a challenging dataset for evaluating computer vision commonsense reasoning abilities. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. Our experiments demonstrate that state-of-the-art models struggle with carefully chosen distractors (±53%, compared to 90% human accuracy). |
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- **Homepage:** |
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https://vasr-dataset.github.io/ |
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- **Colab** |
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https://colab.research.google.com/drive/1HUg0aHonFDK3hVFrIRYdSEfpUJeY-4dI |
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- **Repository:** |
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https://github.com/vasr-dataset/vasr/tree/main/experiments |
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- **Paper:** https://arxiv.org/abs/2212.04542 |
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- **Leaderboard:** |
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https://vasr-dataset.github.io/ |
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- **Point of Contact:** |
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[email protected] |
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## Supported Tasks and Leaderboards |
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https://vasr.github.io/leaderboard. |
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https://paperswithcode.com/dataset/vasr. |
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## How to Submit Predictions? |
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To submit predictions, please send a prediction CSV file to [email protected] / [email protected]. |
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The prediction file should include a "B'" column with the predicted candidate name that best solves the analogy, and an index from 1 to 4 indicating the location of the predicted candidate in the given candidate list. |
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An example prediction file is available [HERE](https://drive.google.com/file/d/1NvBNdvlWmEOYjIVi2xdmQ_tUm-TXo42u/view?usp=share_link). |
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A submission is allowed once a week, and you will receive a response within a week. |
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## Colab notebook code for VASR evaluation with ViT |
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https://colab.research.google.com/drive/1HUg0aHonFDK3hVFrIRYdSEfpUJeY-4dI |
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### Languages |
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English. |
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## Dataset Structure |
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### Data Fields |
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A: datasets.Image() - the first input image, **A**:A'. |
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A': datasets.Image() - the second input image, different from A in a single key, A:**A'**. |
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B: datasets.Image() - the third input image, has the same different item as A, **B**:B'. |
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B': datasets.Image() - the forth image, which is the analogy solution. Different from B in a single key (the same different one as in A:A'), B:**B'**. Hidden in the test set. |
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candidates_images: [datasets.Image()] - a list of candidate images solutions to the analogy. |
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label: datasets.Value("int64") - the index of the ground-truth solution. Hidden in the test set. |
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candidates: [datasets.Value("string")] - a list of candidate string solutions to the analogy. |
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### Data Splits |
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There are three splits, TRAIN, VALIDATION, and TEST. |
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Since there are four candidates and one solution, random chance is 25%. |
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## Dataset Creation |
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We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies. |
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There are two types of labels: |
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- Silver labels, obtained from the automatic generation. |
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- Gold labels, obtained from human annotations over the silver annotations. |
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In the huggingface version we provide only the gold labeled dataset. Please refer to the project website download page if you want to download the silver labels version. |
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### Annotations |
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#### Annotation process |
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We paid Amazon Mechanical Turk Workers to solve analogies, five annotators for each analogy. |
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Workers were asked to select the image that best solves the analogy. |
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The resulting dataset is composed of the 3,820 instances agreed upon with a majority vote of at least 3 annotators, which was obtained in 93% of the cases. |
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## Considerations for Using the Data |
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All associations were obtained with human annotators. |
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All used images are from the imSitu dataset (http://imsitu.org/) |
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Using this data is allowed for academic research alone. |
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### Licensing Information |
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CC-By 4.0 |
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### Citation Information |
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NA |
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