Update README.md

README.md

@@ -13,47 +13,38 @@ And download `image_sets.zip` for all images sets (each directory consisting of
 
 ## Dataset Description
 
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
+- **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
 
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-#### Who are the source language producers?
-
-### Annotations
-
-#### Annotation process
-
-#### Who are the annotators?
-
-
-### Personal and Sensitive Information
-
-### Data Splits
-
-
-## 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.
 
 ### Source Data
 
@@ -93,14 +84,4 @@ And download `image_sets.zip` for all images sets (each directory consisting of
 ### Licensing Information
 
 
-### Citation Information
-
-
-We provide the text description here.
-Image sets can be downloaded on [Zenodo](https://zenodo.org/record/6518944#.YnLboHWZPUQ) or [GoogleDrive](https://drive.google.com/file/d/1OIKNyU0F9lThbaZZ3Jvm7AlF94n1MzDk/view?usp=sharing).
-
-You can download from the commandline via:
-
-```
-wget https://zenodo.org/record/6518944/files/image-sets.zip
-```
+### Citation Information