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---
language:
- en
pretty_name: 'Comics: Pick-A-Panel'
tags:
- comics
dataset_info:
- config_name: char_coherence
features:
- name: sample_id
dtype: string
- 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
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 379249617.0
num_examples: 143
download_size: 379268925
dataset_size: 379249617.0
- config_name: sequence_filling
features:
- name: sample_id
dtype: string
- 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
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 1230082746.0
num_examples: 262
download_size: 1153097954
dataset_size: 1230082746.0
- config_name: text_closure
features:
- name: sample_id
dtype: string
- 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
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 952974973.0
num_examples: 274
download_size: 930660064
dataset_size: 952974973.0
configs:
- config_name: char_coherence
data_files:
- split: val
path: char_coherence/val-*
- config_name: sequence_filling
data_files:
- split: val
path: sequence_filling/val-*
- config_name: text_closure
data_files:
- split: val
path: text_closure/val-*
---
# 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
![Sequence Filling](figures/seq_filling.png)
<details>
<summary>Task Description</summary>
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
![Character Coherence](figures/closure.png)
<details>
<summary>Task Description</summary>
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
![Caption Relevance](figures/caption_relevance.png)
<details>
<summary>Task Description</summary>
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 = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance"
split = "val" # "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_