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--- |
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language: |
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- en |
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pretty_name: 'Comics: Pick-A-Panel' |
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tags: |
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- comics |
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--- |
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# Comics: Pick-A-Panel |
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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) |
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The dataset contains five subtask or skills: |
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### Sequence Filling |
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 |
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<details> |
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<summary>Task Description</summary> |
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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. |
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</details> |
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### Character Coherence, Visual Closure, Text Closure |
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 |
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<details> |
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<summary>Task Description</summary> |
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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: |
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- 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. |
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- 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. |
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- 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. |
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</details> |
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### Caption Relevance |
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 |
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<details> |
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<summary>Task Description</summary> |
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Given a caption from the previous panel, select the panel that best continues the story. |
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</details> |
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## Loading the Data |
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```python |
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from datasets import load_dataset |
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skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance" |
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split = "val" # "val", "test" |
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dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split) |
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``` |
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<details> |
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<summary>Map to single images</summary> |
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If your model can only process single images, you can render each sample as a single image: |
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_coming soon_ |
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</details> |
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## Summit Results and Leaderboard |
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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). |
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## Citation |
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_coming soon_ |