--- 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)
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 ![Character Coherence](figures/closure.png)
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 ![Caption Relevance](figures/caption_relevance.png)
Task Description Given a caption from the previous panel, select the panel that best continues the story.
## 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) ```
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](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_