metadata

language:
  - en
pretty_name: 'Comics: Pick-A-Panel'
dataset_info:
  - config_name: caption_relevance
    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: 530646519
        num_examples: 262
    download_size: 530173119
    dataset_size: 530646519
  - 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
  - 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: 1230081698
        num_examples: 262
    download_size: 1032358336
    dataset_size: 1230081698
configs:
  - config_name: caption_relevance
    data_files:
      - split: val
        path: caption_relevance/val-*
  - config_name: char_coherence
    data_files:
      - split: val
        path: char_coherence/val-*
      - split: test
        path: char_coherence/test-*
  - config_name: sequence_filling
    data_files:
      - split: val
        path: sequence_filling/val-*
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