README.md

---
dataset_info:
- config_name: challenge_data
  features:
  - name: pos_item_ids
    sequence: string
  - name: pos_item_contents
    sequence: string
  - name: question
    dtype: string
  - name: question_id
    dtype: string
  - name: instruction
    dtype: string
  - name: img_path
    dtype: string
  splits:
  - name: train
    num_bytes: 890417
    num_examples: 6415
  download_size: 169300
  dataset_size: 890417
- config_name: challenge_passage
  features:
  - name: passage_id
    dtype: string
  - name: passage_content
    dtype: string
  - name: page_screenshot
    dtype: string
  splits:
  - name: train
    num_bytes: 44091445
    num_examples: 47318
  download_size: 24786149
  dataset_size: 44091445
configs:
- config_name: challenge_data
  data_files:
  - split: train
    path: challenge_data/train-*
- config_name: challenge_passage
  data_files:
  - split: train
    path: challenge_passage/train-*
---
# M2KR-Challenge Dataset

A multimodal retrieval dataset for image-to-document and image+text-to-document matching tasks.

## Dataset Overview

This dataset contains two main subsets designed for multimodal retrieval challenges:
- **challenge_data**: Query data with images and optional text questions (6.42k samples)
- **challenge_passage**: Document collection with textual passages and associated web screenshot path (47.3k passages)

## Dataset Structure

### challenge_data (6,420 rows) 
**Columns**:
- `img_path`: Image filename (string)
- `instruction`: Task instruction for description generation
- `question`: Optional text query (53% populated, if exist then it is a image+text-to-document retrieval task, else, it is a image-to-document task )
- `question_id`: Unique identifier (string)
- `pos_item_ids`: Sequence of positive item IDs, the ground truth passage_id (empty, removed for private test set)
- `pos_item_contents`: Sequence of positive item contents (empty, removed for private test set)

**Task Types**:
1. **Image-to-Document Retrieval**: When `question` is empty (image query)
2. **Multimodal Retrieval**: When `question` contains text (image + text query)

### challenge_passage (47,300 rows)
**Columns**:
- `passage_id`: Unique passage identifier (string)
- `passage_content`: Textual description containing:
  - Image description
  - Structured details about persons (birth/death dates, occupations, locations, etc.)
- `page_screenshot`: Associated image filename (string)

For the retrieval task, you will need to retrieve the corresponding passage from the 47K-passage pool for each sample in challenge_data.

## Images

The image data is provided in separate archives:

- **Web_Image.zip.001**  
- **Web_Image.zip.002**  
- **Web_Image.zip.003**  

These archives contain the web screenshots corresponding to the document passages.

- **query_images.zip**  
  Contains the query images used in the challenge.

---


## References
**Paper or resources for more information:**
- **Paper:** https://arxiv.org/abs/2402.08327
- **Project Page:** https://preflmr.github.io/
- **Huggingface Implementation:** https://github.com/LinWeizheDragon/FLMR


**Citation**
If our work helped your research, please kindly cite our paper for PreFLMR.
```
       
@inproceedings{lin-etal-2024-preflmr,
    title = "{P}re{FLMR}: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers",
    author = "Lin, Weizhe  and
      Mei, Jingbiao  and
      Chen, Jinghong  and
      Byrne, Bill",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.289",
    pages = "5294--5316",
    abstract = "Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions. We present an extensive training and evaluation framework, M2KR, for KB-VQA. M2KR contains a collection of vision and language tasks which we have incorporated into a single suite of benchmark tasks for training and evaluating general-purpose multi-modal retrievers. We use M2KR to develop PreFLMR, a pre-trained version of the recently developed Fine-grained Late-interaction Multi-modal Retriever (FLMR) approach to KB-VQA, and we report new state-of-the-art results across a range of tasks. We also present investigations into the scaling behaviors of PreFLMR intended to be useful in future developments in general-purpose multi-modal retrievers.",
}

```