| --- |
| license: afl-3.0 |
| --- |
| |
| # Dataset Card for ImageCoDe |
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|
| To get started quickly, load descriptions via: |
| ``` |
| from datasets import load_dataset |
| examples = load_dataset('BennoKrojer/ImageCoDe') |
| ``` |
| And download `image_sets.zip` for all images sets (each directory consisting of 10 images). |
|
|
| ## Dataset Description |
|
|
| - **Homepage & Leaderboard:** https://mcgill-nlp.github.io/imagecode/ |
| - **Repository:** https://github.com/McGill-NLP/imagecode |
| - **Paper:** https://arxiv.org/abs/2203.15867 |
| - **Point of Contact:** benno DOT krojer ÄT gmail DOT com |
|
|
| ### 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. |
|
|
| ## 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: |
| ``` |
| {"image_set": "video-storytelling-videowedding_de8dLXvgV-I-shot6_0", |
| "image_index": "8", |
| "description": "The flowers the woman in the teal strapless dress is carrying are completely obscured by the man in the black shirt's head. "} |
| ``` |
| ### Data Splits |
|
|
| | Dataset Split | Number of Descriptions in Split | |
| | ------------- |----------------------------- | |
| | Train | 16,594 | |
| | Validation | 2,302 | |
| | Test | 2,306 | |
|
|
| ## 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. |
