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license: afl-3.0 |
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# Dataset Card for ImageCoDe |
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To get started quickly, load descriptions via: |
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``` |
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from datasets import load_dataset |
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examples = load_dataset('BennoKrojer/ImageCoDe') |
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``` |
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And download `image_sets.zip` for all images sets (each directory consisting of 10 images). |
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## Dataset Description |
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- **Homepage & Leaderboard:** https://mcgill-nlp.github.io/imagecode/ |
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- **Repository:** https://github.com/McGill-NLP/imagecode |
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- **Paper:** https://arxiv.org/abs/2203.15867 |
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- **Point of Contact:** benno DOT krojer ÄT gmail DOT com |
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### Dataset Summary |
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We introduce ImageCoDe, a vision-and-language benchmark that requires contextual language understanding in the form of pragmatics, temporality, long descriptions and visual nuances. The task: Given a detailed description, retrieve the target image among 10 minimally contrastive images. ImageCoDe contains 21K descriptions and 94K images. THe images are primarily frames based on video datasets. |
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## Dataset Structure |
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### Data Instances |
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An instance contains a description, the corresponding image set name, and the target index: |
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``` |
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{"image_set": "video-storytelling-videowedding_de8dLXvgV-I-shot6_0", |
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"image_index": "8", |
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"description": "The flowers the woman in the teal strapless dress is carrying are completely obscured by the man in the black shirt's head. "} |
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``` |
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### Data Splits |
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| Dataset Split | Number of Descriptions in Split | |
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| ------------- |----------------------------- | |
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| Train | 16,594 | |
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| Validation | 2,302 | |
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| Test | 2,306 | |
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## Dataset Creation |
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### Curation Rationale |
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The main goal of ImageCoDe is to highlight weaknesses of recent Vision-and-Language models regarding complex language and fine-grained visual representations. In addition, we found that the dataset offers plenty of pragmatic examples and is therefore suitable for studying pragmatics. |
