---
language:
- en
pretty_name: 'Comics: Pick-A-Panel'
dataset_info:
  config_name: char_coherence
  features:
  - name: context
    sequence: image
  - name: options
    sequence: image
  - name: index
    dtype: int32
  - name: solution_index
    dtype: int32
  - name: split
    dtype: string
  - name: task_type
    dtype: string
  splits:
  - name: val
    num_bytes: 379247043
    num_examples: 143
  - name: test
    num_bytes: 1139804961.0
    num_examples: 489
  download_size: 1518604969
  dataset_size: 1519052004.0
configs:
- config_name: char_coherence
  data_files:
  - split: val
    path: char_coherence/val-*
  - split: test
    path: char_coherence/test-*
tags:
- comics
---

# Comics: Pick-A-Panel

This is the dataset for the [ICDAR 2025 Competition on Comics Understanding in the Era of Foundational Models](https://rrc.cvc.uab.es/?ch=31&com=introduction)

The dataset contains five subtask or skills:

### Sequence Filling
<details>
<summary>Task Description</summary>
![Sequence Filling](figures/seq_filling.png)
Given a sequence of comic panels, a missing panel, and a set of option panels, the task is to select the panel that best fits the sequence.
</details>

### Character Coherence, Visual Closure, Text Closure
<details>
<summary>Task Description</summary>
![Character Coherence](figures/closure.png)
These skills require understanding the context sequence to then pick the best panel to continue the story, focusing on the characters, the visual elements, and the text:
- Character Coherence: Given a sequence of comic panels, pick the panel from the two options that best continues the story in a coherent with the characters. Both options are the same panel, but the text in the speech bubbles is has been swapped.
- Visual Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the visual elements.
- Text Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the text. All options are the same panel, but with text in the speech retrieved from different panels.
</details>

### Caption Relevance
<details>
<summary>Task Description</summary>
![Caption Relevance](figures/caption_relevance.png)
Given a caption from the previous panel, select the panel that best continues the story.
</details>

## Loading the Data

```python
from datasets import load_dataset

skill = "seq_filling" # "seq_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance"
split = "val" # "test"
dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split)
```

<details>
<summary>Map to single images</summary>
If your model can only process single images, you can render each sample as a single image:

_coming soon_

</details>

## Summit Results and Leaderboard
The competition is hosted in the [Robust Reading Competition website](https://rrc.cvc.uab.es/?ch=31&com=introduction) and the leaderboard is available [here](https://rrc.cvc.uab.es/?ch=31&com=evaluation).

## Citation
_coming soon_