README.md

---
dataset_info:
  features:
  - name: wikicaps_id
    dtype: int64
  - name: wikimedia_file
    dtype: string
  - name: caption
    dtype: string
  - name: tokens
    sequence: string
  - name: num_tok
    dtype: int64
  - name: sentence_spans
    sequence: string
  - name: sentence_languages
    sequence: string
  - name: num_sent
    dtype: int64
  - name: min_sent_len
    dtype: int64
  - name: max_sent_len
    dtype: int64
  - name: num_ne
    dtype: int64
  - name: ne_types
    sequence: string
  - name: ne_texts
    sequence: string
  - name: num_nouns
    dtype: int64
  - name: num_propn
    dtype: int64
  - name: num_conj
    dtype: int64
  - name: num_verb
    dtype: int64
  - name: num_sym
    dtype: int64
  - name: num_num
    dtype: int64
  - name: num_adp
    dtype: int64
  - name: num_adj
    dtype: int64
  - name: ratio_ne_tok
    dtype: float64
  - name: ratio_noun_tok
    dtype: float64
  - name: ratio_propn_tok
    dtype: float64
  - name: ratio_all_noun_tok
    dtype: float64
  - name: image_path
    dtype: string
  splits:
  - name: train
    num_bytes: 398344229
    num_examples: 295886
  - name: test
    num_bytes: 6727191
    num_examples: 5000
  download_size: 183918204
  dataset_size: 405071420
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: cc-by-sa-4.0
language:
- en
pretty_name: WISMIR 3
size_categories:
- 100K<n<1M
---
# WISMIR3: A Multi-Modal Dataset to Challenge Text-Image Retrieval Approaches

This repository holds the WISMIR3 dataset. For more information, please refer to the paper:

```bibtex
@inproceedings{
schneider2024wismir,
title={{WISMIR}3: A Multi-Modal Dataset to Challenge Text-Image Retrieval Approaches},
author={Florian Schneider and Chris Biemann},
booktitle={3rd Workshop on Advances in Language and Vision Research (ALVR)},
year={2024},
url={https://openreview.net/forum?id=Q93yqpfECQ}
}
```

## Download Images
To get the images:
1. Download all image parts from `images` directory.
2. Join the parts into a single archive file `cat images/images.tar.gz.part* > images/images.tar.gz`
3. Extract the archive `tar xvzf images.tar.gz`

## Download Pre-computed Embeddings
To get the embeddings:
1. Download all image parts from the `features` directory. `frcnn_feats` are Faster-R-CNN Features, `clip_ViT-B-16` are CLIP embeddings. For more information, please refer to the paper.
2.1. Join the parts into a single archive file `cat features/clip_ViT-B-16_embs.tar.gz.part_* > features/clip_ViT-B-16_embs.tar.gz`
2.2. Join the parts into a single archive file `cat features/frcnn_feats.tar.gz.part_* > features/frcnn_feats.tar.gz`
3.1. Extract the archive `tar xvzf features/clip_ViT-B-16_embs.tar.gz`
3.2. Extract the archive `tar xvzf features/frcnn_feats.tar.gz`

## Columns

| ColumnId			| Description																| Datatype	|
|-------------------|---------------------------------------------------------------------------|-----------|
| wikicaps_id		| ID (line number) of the row in the original WikiCaps Dataset __img_en__ 	| int		|
| wikimedia_file    | Wikimedia File ID of the Image associated with the Caption				| str		|
| caption			| Caption of the Image														| str		|
| image_path		| Local path to the (downloaded) image										| str		|
| num_tok			| Number of Tokens in the caption											| int		|
| num_sent			| Number of Sentences in the caption										| int		|
| min_sent_len		| Minimum number of Tokens in the Sentences of the caption					| int		|
| max_sent_len		| Maximum number of Tokens in the Sentences of the caption					| int		|
| num_ne			| Number of Named Entities in the caption									| int		|
| num_nouns			| Number of Tokens with NOUN POS Tag 										| int		|
| num_propn			| Number of Tokens with PROPN POS Tag   									| int		|
| num_conj			| Number of Tokens with CONJ POS Tag 										| int		|
| num_verb			| Number of Tokens with VERB POS Tag 										| int		|
| num_sym			| Number of Tokens with SYM POS Tag 										| int		|
| num_num			| Number of Tokens with NUM POS Tag 										| int		|
| num_adp			| Number of Tokens with ADP POS Tag 										| int		|
| num_adj			| Number of Tokens with ADJ POS Tag 										| int		|
| ratio_ne_tok		| Ratio of tokens associated with Named Entities vs all Tokens  			| int		|
| ratio_noun_tok	| Ratio of tokens tagged as NOUN vs all Tokens  							| int		|
| ratio_propn_tok	| Ratio of tokens tagged as PROPN vs all Tokens  							| int		|
| ratio_all_noun_tok| Ratio of tokens tagged as PROPN or NOUN vs all Tokens     				| int		|
| fk_re_score		| Flesch-Kincaid Reading Ease score of the Caption ***						| int		|
| fk_gl_score		| Flesch-Kincaid Grade Level score of the Caption ***						| int		|
| dc_score			| Dale-Chall score of the Caption ***										| int		|
| ne_texts			| Surface form of detected NamedEntities									| List[str]	|
| ne_types			| Types of the detected NamedEntities (PER, LOC, GPE, etc.)					| List[str]	|

***
See [https://en.wikipedia.org/wiki/List_of_readability_tests_and_formulas](https://en.wikipedia.org/wiki/List_of_readability_tests_and_formulas) for more information about
Readability Scores

## WikiCaps publication
WISMIR3 is based on the WikiCaps dataset. For more information about the WikiCaps, see [https://www.cl.uni-heidelberg.de/statnlpgroup/wikicaps/](https://www.cl.uni-heidelberg.de/statnlpgroup/wikicaps/)

```bibtex
@inproceedings{schamoni-etal-2018-dataset,
    title = "A Dataset and Reranking Method for Multimodal {MT} of User-Generated Image Captions",
    author = "Schamoni, Shigehiko  and
      Hitschler, Julian  and
      Riezler, Stefan",
    editor = "Cherry, Colin  and
      Neubig, Graham",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/W18-1814",
    pages = "140--153",
}

```