Datasets:
File size: 3,079 Bytes
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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>
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>
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>
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_ |