README.md

metadata

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
      num_examples: 489
  download_size: 1518604969
  dataset_size: 1519052004
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

The dataset contains five subtask or skills:

Sequence Filling

Task Description

 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.

Character Coherence, Visual Closure, Text Closure

Task Description

 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.

Caption Relevance

Task Description

 Given a caption from the previous panel, select the panel that best continues the story.

Loading the Data

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)

Map to single images

If your model can only process single images, you can render each sample as a single image:

coming soon

Summit Results and Leaderboard

The competition is hosted in the Robust Reading Competition website and the leaderboard is available here.

Citation

coming soon