license: afl-3.0
Dataset Card for ImageCoDe
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
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
Data Instances
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
Curation Rationale
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.